{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25a08670",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import seaborn as sn\n",
    "import datetime\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import optimizers, losses, activations, models\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.layers import Flatten, Dense, Conv1D, MaxPool1D, Dropout, LSTM\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import class_weight\n",
    "from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ef8eebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Focal Loss\n",
    "def sparse_categorical_focal_loss(alpha=0.25, gamma=2.0):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = tf.squeeze(tf.cast(y_true, tf.int32), axis=-1)\n",
    "        y_true_one_hot = tf.one_hot(y_true, depth=tf.shape(y_pred)[-1])\n",
    "        cross_entropy = tf.keras.losses.categorical_crossentropy(y_true_one_hot, y_pred)\n",
    "        probs = tf.reduce_sum(y_true_one_hot * y_pred, axis=-1)\n",
    "        focal_weight = tf.pow(1.0 - probs, gamma)\n",
    "        focal_loss = alpha * focal_weight * cross_entropy\n",
    "        return focal_loss\n",
    "    return loss\n",
    "\n",
    "# Class weights computation\n",
    "def compute_smart_class_weights(y_train, boost_factor=2.0, abnormal_boost=3.0):\n",
    "    raw_class_weights = class_weight.compute_class_weight(class_weight='balanced',\n",
    "                                                           classes=np.unique(y_train),\n",
    "                                                           y=y_train)\n",
    "    class_weight_dict = dict(enumerate(raw_class_weights))\n",
    "    majority_class = min(class_weight_dict, key=class_weight_dict.get)\n",
    "    majority_weight = class_weight_dict[majority_class]\n",
    "    for cls, weight in class_weight_dict.items():\n",
    "        if cls != majority_class:\n",
    "            class_weight_dict[cls] = max(weight, boost_factor * majority_weight)\n",
    "    abnormal_classes = [1, 2, 3]\n",
    "    for cls in abnormal_classes:\n",
    "        if cls in class_weight_dict:\n",
    "            class_weight_dict[cls] *= abnormal_boost\n",
    "    print(\"Smart computed and boosted class weights:\", class_weight_dict)\n",
    "    return class_weight_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ff3505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_5 (Conv1D)           (None, 187, 32)           192       \n",
      "                                                                 \n",
      " conv1d_6 (Conv1D)           (None, 187, 64)           10304     \n",
      "                                                                 \n",
      " conv1d_7 (Conv1D)           (None, 187, 64)           20544     \n",
      "                                                                 \n",
      " conv1d_8 (Conv1D)           (None, 187, 128)          41088     \n",
      "                                                                 \n",
      " conv1d_9 (Conv1D)           (None, 187, 128)          82048     \n",
      "                                                                 \n",
      " max_pooling1d_3 (MaxPooling  (None, 94, 128)          0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 94, 128)           0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 94, 210)           284760    \n",
      "                                                                 \n",
      " max_pooling1d_4 (MaxPooling  (None, 47, 210)          0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 9870)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 5)                 49355     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 5)                 30        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 488,321\n",
      "Trainable params: 488,321\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Smart computed and boosted class weights: {0: 0.2416550133706239, 1: 23.527308917197452, 2: 9.042319461444308, 3: 81.63066298342541, 4: 2.7334813375884557}\n",
      "Epoch 1/100\n",
      "770/770 [==============================] - 164s 209ms/step - loss: 0.3140 - accuracy: 0.1148 - val_loss: 0.2814 - val_accuracy: 0.1915 - lr: 5.0000e-04\n",
      "Epoch 2/100\n",
      "770/770 [==============================] - 224s 291ms/step - loss: 0.1506 - accuracy: 0.5827 - val_loss: 0.1771 - val_accuracy: 0.7011 - lr: 5.0000e-04\n",
      "Epoch 3/100\n",
      "770/770 [==============================] - 176s 228ms/step - loss: 0.1113 - accuracy: 0.7523 - val_loss: 0.1054 - val_accuracy: 0.8026 - lr: 5.0000e-04\n",
      "Epoch 4/100\n",
      "770/770 [==============================] - 164s 213ms/step - loss: 0.0898 - accuracy: 0.7845 - val_loss: 0.1361 - val_accuracy: 0.7179 - lr: 5.0000e-04\n",
      "Epoch 5/100\n",
      "770/770 [==============================] - 183s 238ms/step - loss: 0.0776 - accuracy: 0.8083 - val_loss: 0.0608 - val_accuracy: 0.8574 - lr: 5.0000e-04\n",
      "Epoch 6/100\n",
      "770/770 [==============================] - 155s 202ms/step - loss: 0.0668 - accuracy: 0.8183 - val_loss: 0.0542 - val_accuracy: 0.8649 - lr: 5.0000e-04\n",
      "Epoch 7/100\n",
      "770/770 [==============================] - 181s 236ms/step - loss: 0.0554 - accuracy: 0.8389 - val_loss: 0.0523 - val_accuracy: 0.8672 - lr: 5.0000e-04\n",
      "Epoch 8/100\n",
      "770/770 [==============================] - 188s 245ms/step - loss: 0.0490 - accuracy: 0.8507 - val_loss: 0.0433 - val_accuracy: 0.8905 - lr: 5.0000e-04\n",
      "Epoch 9/100\n",
      "770/770 [==============================] - 200s 260ms/step - loss: 0.0454 - accuracy: 0.8559 - val_loss: 0.0417 - val_accuracy: 0.8862 - lr: 5.0000e-04\n",
      "Epoch 10/100\n",
      "770/770 [==============================] - 158s 205ms/step - loss: 0.0404 - accuracy: 0.8705 - val_loss: 0.0364 - val_accuracy: 0.8979 - lr: 5.0000e-04\n",
      "Epoch 11/100\n",
      "770/770 [==============================] - 157s 203ms/step - loss: 0.0399 - accuracy: 0.8704 - val_loss: 0.0608 - val_accuracy: 0.8460 - lr: 5.0000e-04\n",
      "Epoch 12/100\n",
      "770/770 [==============================] - 203s 264ms/step - loss: 0.0331 - accuracy: 0.8842 - val_loss: 0.0432 - val_accuracy: 0.8807 - lr: 5.0000e-04\n",
      "Epoch 13/100\n",
      "770/770 [==============================] - 190s 246ms/step - loss: 0.0320 - accuracy: 0.8875 - val_loss: 0.0375 - val_accuracy: 0.8869 - lr: 5.0000e-04\n",
      "Epoch 14/100\n",
      "770/770 [==============================] - 154s 200ms/step - loss: 0.0320 - accuracy: 0.8877 - val_loss: 0.0271 - val_accuracy: 0.9163 - lr: 5.0000e-04\n",
      "Epoch 15/100\n",
      "770/770 [==============================] - 51s 67ms/step - loss: 0.0254 - accuracy: 0.9049 - val_loss: 0.0339 - val_accuracy: 0.8941 - lr: 5.0000e-04\n",
      "Epoch 16/100\n",
      "770/770 [==============================] - 56s 73ms/step - loss: 0.0256 - accuracy: 0.8974 - val_loss: 0.0264 - val_accuracy: 0.9194 - lr: 5.0000e-04\n",
      "Epoch 17/100\n",
      "770/770 [==============================] - 50s 66ms/step - loss: 0.0295 - accuracy: 0.8895 - val_loss: 0.0322 - val_accuracy: 0.8934 - lr: 5.0000e-04\n",
      "Epoch 18/100\n",
      "770/770 [==============================] - 48s 62ms/step - loss: 0.0212 - accuracy: 0.9112 - val_loss: 0.0296 - val_accuracy: 0.9138 - lr: 5.0000e-04\n",
      "Epoch 19/100\n",
      "770/770 [==============================] - 47s 62ms/step - loss: 0.0211 - accuracy: 0.9139 - val_loss: 0.0295 - val_accuracy: 0.9043 - lr: 5.0000e-04\n",
      "Epoch 20/100\n",
      "770/770 [==============================] - 54s 70ms/step - loss: 0.0215 - accuracy: 0.9099 - val_loss: 0.0201 - val_accuracy: 0.9405 - lr: 5.0000e-04\n",
      "Epoch 21/100\n",
      "770/770 [==============================] - 53s 69ms/step - loss: 0.0217 - accuracy: 0.9098 - val_loss: 0.0392 - val_accuracy: 0.8885 - lr: 5.0000e-04\n",
      "Epoch 22/100\n",
      "770/770 [==============================] - 51s 67ms/step - loss: 0.0165 - accuracy: 0.9267 - val_loss: 0.0351 - val_accuracy: 0.8734 - lr: 5.0000e-04\n",
      "Epoch 23/100\n",
      "770/770 [==============================] - 55s 71ms/step - loss: 0.0168 - accuracy: 0.9287 - val_loss: 0.0213 - val_accuracy: 0.9262 - lr: 5.0000e-04\n",
      "Epoch 24/100\n",
      "770/770 [==============================] - 71s 92ms/step - loss: 0.0143 - accuracy: 0.9369 - val_loss: 0.0342 - val_accuracy: 0.8845 - lr: 5.0000e-04\n",
      "Epoch 25/100\n",
      "770/770 [==============================] - 69s 90ms/step - loss: 0.0108 - accuracy: 0.9491 - val_loss: 0.0122 - val_accuracy: 0.9612 - lr: 2.5000e-04\n",
      "Epoch 26/100\n",
      "770/770 [==============================] - 83s 108ms/step - loss: 0.0089 - accuracy: 0.9576 - val_loss: 0.0143 - val_accuracy: 0.9540 - lr: 2.5000e-04\n",
      "Epoch 27/100\n",
      "770/770 [==============================] - 153s 199ms/step - loss: 0.0099 - accuracy: 0.9577 - val_loss: 0.0142 - val_accuracy: 0.9562 - lr: 2.5000e-04\n",
      "Epoch 28/100\n",
      "770/770 [==============================] - 171s 222ms/step - loss: 0.0089 - accuracy: 0.9593 - val_loss: 0.0118 - val_accuracy: 0.9585 - lr: 2.5000e-04\n",
      "Epoch 29/100\n",
      "770/770 [==============================] - 191s 248ms/step - loss: 0.0083 - accuracy: 0.9611 - val_loss: 0.0128 - val_accuracy: 0.9591 - lr: 2.5000e-04\n",
      "Epoch 30/100\n",
      "770/770 [==============================] - 175s 227ms/step - loss: 0.0079 - accuracy: 0.9639 - val_loss: 0.0147 - val_accuracy: 0.9517 - lr: 2.5000e-04\n",
      "Epoch 31/100\n",
      "770/770 [==============================] - 170s 220ms/step - loss: 0.0077 - accuracy: 0.9614 - val_loss: 0.0128 - val_accuracy: 0.9588 - lr: 2.5000e-04\n",
      "Epoch 32/100\n",
      "770/770 [==============================] - 178s 231ms/step - loss: 0.0089 - accuracy: 0.9591 - val_loss: 0.0119 - val_accuracy: 0.9613 - lr: 2.5000e-04\n",
      "Epoch 33/100\n",
      "770/770 [==============================] - 158s 205ms/step - loss: 0.0056 - accuracy: 0.9721 - val_loss: 0.0102 - val_accuracy: 0.9672 - lr: 1.2500e-04\n",
      "Epoch 34/100\n",
      "770/770 [==============================] - 156s 203ms/step - loss: 0.0051 - accuracy: 0.9746 - val_loss: 0.0101 - val_accuracy: 0.9688 - lr: 1.2500e-04\n",
      "Epoch 35/100\n",
      "770/770 [==============================] - 159s 206ms/step - loss: 0.0047 - accuracy: 0.9780 - val_loss: 0.0095 - val_accuracy: 0.9693 - lr: 1.2500e-04\n",
      "Epoch 36/100\n",
      "770/770 [==============================] - 161s 210ms/step - loss: 0.0045 - accuracy: 0.9783 - val_loss: 0.0086 - val_accuracy: 0.9726 - lr: 1.2500e-04\n",
      "Epoch 37/100\n",
      "770/770 [==============================] - 284s 368ms/step - loss: 0.0049 - accuracy: 0.9775 - val_loss: 0.0082 - val_accuracy: 0.9751 - lr: 1.2500e-04\n",
      "Epoch 38/100\n",
      "770/770 [==============================] - 177s 229ms/step - loss: 0.0045 - accuracy: 0.9786 - val_loss: 0.0082 - val_accuracy: 0.9746 - lr: 1.2500e-04\n",
      "Epoch 39/100\n",
      "770/770 [==============================] - 158s 205ms/step - loss: 0.0043 - accuracy: 0.9817 - val_loss: 0.0094 - val_accuracy: 0.9688 - lr: 1.2500e-04\n",
      "Epoch 40/100\n",
      "770/770 [==============================] - 181s 236ms/step - loss: 0.0044 - accuracy: 0.9815 - val_loss: 0.0096 - val_accuracy: 0.9708 - lr: 1.2500e-04\n",
      "Epoch 41/100\n",
      "770/770 [==============================] - 186s 241ms/step - loss: 0.0038 - accuracy: 0.9819 - val_loss: 0.0086 - val_accuracy: 0.9742 - lr: 1.2500e-04\n",
      "Epoch 42/100\n",
      "770/770 [==============================] - 155s 202ms/step - loss: 0.0038 - accuracy: 0.9828 - val_loss: 0.0079 - val_accuracy: 0.9765 - lr: 6.2500e-05\n",
      "Epoch 43/100\n",
      "770/770 [==============================] - 171s 222ms/step - loss: 0.0032 - accuracy: 0.9849 - val_loss: 0.0075 - val_accuracy: 0.9760 - lr: 6.2500e-05\n",
      "Epoch 44/100\n",
      "770/770 [==============================] - 153s 199ms/step - loss: 0.0030 - accuracy: 0.9865 - val_loss: 0.0073 - val_accuracy: 0.9787 - lr: 6.2500e-05\n",
      "Epoch 45/100\n",
      "770/770 [==============================] - 153s 199ms/step - loss: 0.0030 - accuracy: 0.9871 - val_loss: 0.0070 - val_accuracy: 0.9803 - lr: 6.2500e-05\n",
      "Epoch 46/100\n",
      "770/770 [==============================] - 162s 211ms/step - loss: 0.0030 - accuracy: 0.9861 - val_loss: 0.0068 - val_accuracy: 0.9804 - lr: 6.2500e-05\n",
      "Epoch 47/100\n",
      "770/770 [==============================] - 153s 199ms/step - loss: 0.0030 - accuracy: 0.9863 - val_loss: 0.0067 - val_accuracy: 0.9788 - lr: 6.2500e-05\n",
      "Epoch 48/100\n",
      "770/770 [==============================] - 178s 231ms/step - loss: 0.0030 - accuracy: 0.9877 - val_loss: 0.0071 - val_accuracy: 0.9793 - lr: 6.2500e-05\n",
      "Epoch 49/100\n",
      "770/770 [==============================] - 1883s 2s/step - loss: 0.0027 - accuracy: 0.9881 - val_loss: 0.0071 - val_accuracy: 0.9807 - lr: 6.2500e-05\n",
      "Epoch 50/100\n",
      "770/770 [==============================] - 26s 33ms/step - loss: 0.0029 - accuracy: 0.9882 - val_loss: 0.0071 - val_accuracy: 0.9792 - lr: 6.2500e-05\n",
      "Epoch 51/100\n",
      "770/770 [==============================] - 24s 31ms/step - loss: 0.0025 - accuracy: 0.9895 - val_loss: 0.0070 - val_accuracy: 0.9791 - lr: 3.1250e-05\n",
      "Epoch 52/100\n",
      "770/770 [==============================] - 28s 37ms/step - loss: 0.0025 - accuracy: 0.9891 - val_loss: 0.0070 - val_accuracy: 0.9794 - lr: 3.1250e-05\n",
      "Epoch 53/100\n",
      "770/770 [==============================] - 35s 45ms/step - loss: 0.0023 - accuracy: 0.9900 - val_loss: 0.0062 - val_accuracy: 0.9823 - lr: 3.1250e-05\n",
      "Epoch 54/100\n",
      "770/770 [==============================] - 124s 161ms/step - loss: 0.0023 - accuracy: 0.9900 - val_loss: 0.0068 - val_accuracy: 0.9808 - lr: 3.1250e-05\n",
      "Epoch 55/100\n",
      "770/770 [==============================] - 153s 199ms/step - loss: 0.0023 - accuracy: 0.9903 - val_loss: 0.0062 - val_accuracy: 0.9824 - lr: 3.1250e-05\n",
      "Epoch 56/100\n",
      "770/770 [==============================] - 153s 199ms/step - loss: 0.0023 - accuracy: 0.9909 - val_loss: 0.0066 - val_accuracy: 0.9818 - lr: 3.1250e-05\n",
      "Epoch 57/100\n",
      "770/770 [==============================] - 224s 291ms/step - loss: 0.0021 - accuracy: 0.9910 - val_loss: 0.0066 - val_accuracy: 0.9825 - lr: 3.1250e-05\n",
      "Epoch 58/100\n",
      "770/770 [==============================] - 108s 140ms/step - loss: 0.0021 - accuracy: 0.9911 - val_loss: 0.0067 - val_accuracy: 0.9818 - lr: 1.5625e-05\n",
      "Epoch 59/100\n",
      "770/770 [==============================] - 65s 84ms/step - loss: 0.0020 - accuracy: 0.9913 - val_loss: 0.0064 - val_accuracy: 0.9833 - lr: 1.5625e-05\n",
      "Epoch 60/100\n",
      "770/770 [==============================] - 69s 90ms/step - loss: 0.0019 - accuracy: 0.9919 - val_loss: 0.0064 - val_accuracy: 0.9828 - lr: 1.5625e-05\n",
      "Epoch 61/100\n",
      "770/770 [==============================] - 67s 87ms/step - loss: 0.0021 - accuracy: 0.9911 - val_loss: 0.0064 - val_accuracy: 0.9826 - lr: 1.5625e-05\n",
      "Epoch 62/100\n",
      "770/770 [==============================] - 63s 82ms/step - loss: 0.0020 - accuracy: 0.9914 - val_loss: 0.0063 - val_accuracy: 0.9836 - lr: 7.8125e-06\n",
      "Epoch 63/100\n",
      "770/770 [==============================] - 67s 87ms/step - loss: 0.0019 - accuracy: 0.9919 - val_loss: 0.0062 - val_accuracy: 0.9839 - lr: 7.8125e-06\n"
     ]
    }
   ],
   "source": [
    "# Load MIT-BIH dataset\n",
    "df_train = pd.read_csv(\"data\\mitbih_train.csv\", header=None)\n",
    "df_test = pd.read_csv(\"data\\mitbih_test.csv\", header=None)\n",
    "df_combine = pd.concat([df_train, df_test])\n",
    "\n",
    "X = df_combine.iloc[:, :-1].values\n",
    "y = df_combine.iloc[:, -1].astype(int).values\n",
    "\n",
    "X = np.expand_dims(X, axis=-1)\n",
    "\n",
    "# Train/Val split\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, random_state=42)\n",
    "\n",
    "# Build model\n",
    "model = models.Sequential([\n",
    "    Conv1D(32, 5, padding='same', activation=tf.keras.layers.LeakyReLU(alpha=0.001), input_shape=(187, 1)),\n",
    "    Conv1D(64, 5, padding='same', activation=tf.keras.layers.LeakyReLU(alpha=0.001)),\n",
    "    Conv1D(64, 5, padding='same', activation=tf.keras.layers.LeakyReLU(alpha=0.001)),\n",
    "    Conv1D(128, 5, padding='same', activation=tf.keras.layers.LeakyReLU(alpha=0.001)),\n",
    "    Conv1D(128, 5, padding='same', activation=tf.keras.layers.LeakyReLU(alpha=0.001)),\n",
    "    MaxPool1D(pool_size=5, strides=2, padding='same'),\n",
    "    Dropout(0.45),\n",
    "    LSTM(210, return_sequences=True),\n",
    "    MaxPool1D(pool_size=5, strides=2, padding='same'),\n",
    "    Flatten(),\n",
    "    Dense(5, activation='relu'),\n",
    "    Dense(5, activation='softmax')\n",
    "])\n",
    "\n",
    "opt = optimizers.Adam(learning_rate=0.0005)\n",
    "model.compile(optimizer=opt, loss=sparse_categorical_focal_loss(alpha=0.25, gamma=2.0), metrics=['accuracy'])\n",
    "model.summary()\n",
    "\n",
    "# Training\n",
    "es_cb = EarlyStopping(monitor='val_loss', patience=8, restore_best_weights=True)\n",
    "lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=4, min_lr=1e-6)\n",
    "\n",
    "class_weights = compute_smart_class_weights(y_train)\n",
    "\n",
    "history = model.fit(X_train, y_train, validation_data=(X_val, y_val),\n",
    "                    epochs=100, batch_size=128, callbacks=[es_cb, lr_reduce],\n",
    "                    class_weight=class_weights)\n",
    "\n",
    "# Save the trained model\n",
    "model.save(\"best_mitbih_model.h5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0cb2867",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save training history (history object)\n",
    "import pickle\n",
    "\n",
    "with open(\"best_mitbih_model.pkl\", \"wb\") as f:\n",
    "    pickle.dump(history.history, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "156ae8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_mitbih = load_model(\"best_mitbih_model.h5\", custom_objects={'loss': sparse_categorical_focal_loss(alpha=0.25, gamma=2.0)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79e3bd25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "685/685 [==============================] - 9s 14ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Predict\n",
    "y_pred_probs = model_mitbih.predict(np.expand_dims(df_test.iloc[:, :-1].values, axis=-1))  # shape: (num_samples, num_classes)\n",
    "y_pred = np.argmax(y_pred_probs, axis=1)\n",
    "\n",
    "# Step 2: Confusion matrix\n",
    "cm = confusion_matrix(df_test.iloc[:,-1], y_pred)\n",
    "\n",
    "# Step 3: Plot\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=['N','S','V','F','U'])  # MIT-BIH 5 classes\n",
    "disp.plot(cmap=\"Blues\", values_format=\"d\")\n",
    "plt.title(\"Confusion Matrix on MIT-BIH Test Set\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b0bb6639",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           N       1.00      0.99      1.00     18118\n",
      "           S       0.89      0.99      0.94       556\n",
      "           V       0.99      0.99      0.99      1448\n",
      "           F       0.65      1.00      0.78       162\n",
      "           U       1.00      1.00      1.00      1608\n",
      "\n",
      "    accuracy                           0.99     21892\n",
      "   macro avg       0.90      0.99      0.94     21892\n",
      "weighted avg       0.99      0.99      0.99     21892\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate full classification report\n",
    "print(classification_report(df_test.iloc[:,-1], y_pred, target_names=[\"N\", \"S\", \"V\", \"F\", \"U\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db9456df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "91/91 [==============================] - 5s 31ms/step - loss: 0.4968 - accuracy: 0.3808 - val_loss: 0.2621 - val_accuracy: 0.4954 - lr: 1.0000e-04\n",
      "Epoch 2/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.1785 - accuracy: 0.6066 - val_loss: 0.1360 - val_accuracy: 0.6664 - lr: 1.0000e-04\n",
      "Epoch 3/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.1099 - accuracy: 0.7048 - val_loss: 0.0952 - val_accuracy: 0.7375 - lr: 1.0000e-04\n",
      "Epoch 4/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0821 - accuracy: 0.7537 - val_loss: 0.0753 - val_accuracy: 0.7743 - lr: 1.0000e-04\n",
      "Epoch 5/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0653 - accuracy: 0.7853 - val_loss: 0.0624 - val_accuracy: 0.7953 - lr: 1.0000e-04\n",
      "Epoch 6/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0560 - accuracy: 0.8034 - val_loss: 0.0532 - val_accuracy: 0.8090 - lr: 1.0000e-04\n",
      "Epoch 7/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0482 - accuracy: 0.8177 - val_loss: 0.0462 - val_accuracy: 0.8234 - lr: 1.0000e-04\n",
      "Epoch 8/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0408 - accuracy: 0.8338 - val_loss: 0.0403 - val_accuracy: 0.8341 - lr: 1.0000e-04\n",
      "Epoch 9/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0364 - accuracy: 0.8439 - val_loss: 0.0360 - val_accuracy: 0.8430 - lr: 1.0000e-04\n",
      "Epoch 10/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0326 - accuracy: 0.8528 - val_loss: 0.0323 - val_accuracy: 0.8537 - lr: 1.0000e-04\n",
      "Epoch 11/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0306 - accuracy: 0.8595 - val_loss: 0.0289 - val_accuracy: 0.8667 - lr: 1.0000e-04\n",
      "Epoch 12/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0272 - accuracy: 0.8689 - val_loss: 0.0263 - val_accuracy: 0.8746 - lr: 1.0000e-04\n",
      "Epoch 13/50\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0254 - accuracy: 0.8736 - val_loss: 0.0241 - val_accuracy: 0.8811 - lr: 1.0000e-04\n",
      "Epoch 14/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0228 - accuracy: 0.8812 - val_loss: 0.0221 - val_accuracy: 0.8846 - lr: 1.0000e-04\n",
      "Epoch 15/50\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0216 - accuracy: 0.8834 - val_loss: 0.0208 - val_accuracy: 0.8877 - lr: 1.0000e-04\n",
      "Epoch 16/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0196 - accuracy: 0.8900 - val_loss: 0.0193 - val_accuracy: 0.8942 - lr: 1.0000e-04\n",
      "Epoch 17/50\n",
      "91/91 [==============================] - 2s 19ms/step - loss: 0.0185 - accuracy: 0.8924 - val_loss: 0.0181 - val_accuracy: 0.8987 - lr: 1.0000e-04\n",
      "Epoch 18/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0173 - accuracy: 0.9013 - val_loss: 0.0172 - val_accuracy: 0.9024 - lr: 1.0000e-04\n",
      "Epoch 19/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0164 - accuracy: 0.9052 - val_loss: 0.0162 - val_accuracy: 0.9093 - lr: 1.0000e-04\n",
      "Epoch 20/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0152 - accuracy: 0.9069 - val_loss: 0.0156 - val_accuracy: 0.9090 - lr: 1.0000e-04\n",
      "Epoch 21/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0150 - accuracy: 0.9104 - val_loss: 0.0149 - val_accuracy: 0.9131 - lr: 1.0000e-04\n",
      "Epoch 22/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0142 - accuracy: 0.9176 - val_loss: 0.0142 - val_accuracy: 0.9165 - lr: 1.0000e-04\n",
      "Epoch 23/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0135 - accuracy: 0.9204 - val_loss: 0.0136 - val_accuracy: 0.9193 - lr: 1.0000e-04\n",
      "Epoch 24/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0131 - accuracy: 0.9203 - val_loss: 0.0131 - val_accuracy: 0.9244 - lr: 1.0000e-04\n",
      "Epoch 25/50\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0126 - accuracy: 0.9238 - val_loss: 0.0127 - val_accuracy: 0.9268 - lr: 1.0000e-04\n",
      "Epoch 26/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0120 - accuracy: 0.9274 - val_loss: 0.0126 - val_accuracy: 0.9261 - lr: 1.0000e-04\n",
      "Epoch 27/50\n",
      "91/91 [==============================] - 1s 17ms/step - loss: 0.0116 - accuracy: 0.9297 - val_loss: 0.0120 - val_accuracy: 0.9303 - lr: 1.0000e-04\n",
      "Epoch 28/50\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0115 - accuracy: 0.9292 - val_loss: 0.0116 - val_accuracy: 0.9330 - lr: 1.0000e-04\n",
      "Epoch 29/50\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0108 - accuracy: 0.9339 - val_loss: 0.0114 - val_accuracy: 0.9337 - lr: 1.0000e-04\n",
      "Epoch 30/50\n",
      "91/91 [==============================] - 2s 20ms/step - loss: 0.0106 - accuracy: 0.9380 - val_loss: 0.0109 - val_accuracy: 0.9368 - lr: 1.0000e-04\n",
      "Epoch 31/50\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0103 - accuracy: 0.9378 - val_loss: 0.0107 - val_accuracy: 0.9392 - lr: 1.0000e-04\n",
      "Epoch 32/50\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0100 - accuracy: 0.9406 - val_loss: 0.0103 - val_accuracy: 0.9437 - lr: 1.0000e-04\n",
      "Epoch 33/50\n",
      "91/91 [==============================] - 2s 20ms/step - loss: 0.0096 - accuracy: 0.9427 - val_loss: 0.0103 - val_accuracy: 0.9409 - lr: 1.0000e-04\n",
      "Epoch 34/50\n",
      "91/91 [==============================] - 2s 22ms/step - loss: 0.0092 - accuracy: 0.9472 - val_loss: 0.0102 - val_accuracy: 0.9402 - lr: 1.0000e-04\n",
      "Epoch 35/50\n",
      "91/91 [==============================] - 2s 23ms/step - loss: 0.0091 - accuracy: 0.9475 - val_loss: 0.0097 - val_accuracy: 0.9461 - lr: 1.0000e-04\n",
      "Epoch 36/50\n",
      "91/91 [==============================] - 2s 19ms/step - loss: 0.0089 - accuracy: 0.9492 - val_loss: 0.0096 - val_accuracy: 0.9447 - lr: 1.0000e-04\n",
      "Epoch 37/50\n",
      "91/91 [==============================] - 2s 23ms/step - loss: 0.0087 - accuracy: 0.9497 - val_loss: 0.0092 - val_accuracy: 0.9512 - lr: 1.0000e-04\n",
      "Epoch 38/50\n",
      "91/91 [==============================] - 2s 20ms/step - loss: 0.0086 - accuracy: 0.9509 - val_loss: 0.0091 - val_accuracy: 0.9495 - lr: 1.0000e-04\n",
      "Epoch 39/50\n",
      "91/91 [==============================] - 2s 20ms/step - loss: 0.0082 - accuracy: 0.9549 - val_loss: 0.0089 - val_accuracy: 0.9512 - lr: 1.0000e-04\n",
      "Epoch 40/50\n",
      "91/91 [==============================] - 2s 21ms/step - loss: 0.0081 - accuracy: 0.9565 - val_loss: 0.0087 - val_accuracy: 0.9533 - lr: 1.0000e-04\n",
      "Epoch 41/50\n",
      "91/91 [==============================] - 2s 20ms/step - loss: 0.0079 - accuracy: 0.9541 - val_loss: 0.0089 - val_accuracy: 0.9509 - lr: 1.0000e-04\n",
      "Epoch 42/50\n",
      "91/91 [==============================] - 2s 20ms/step - loss: 0.0077 - accuracy: 0.9573 - val_loss: 0.0085 - val_accuracy: 0.9529 - lr: 1.0000e-04\n",
      "Epoch 43/50\n",
      "91/91 [==============================] - 2s 22ms/step - loss: 0.0075 - accuracy: 0.9582 - val_loss: 0.0081 - val_accuracy: 0.9595 - lr: 1.0000e-04\n",
      "Epoch 44/50\n",
      "91/91 [==============================] - 2s 22ms/step - loss: 0.0074 - accuracy: 0.9573 - val_loss: 0.0080 - val_accuracy: 0.9588 - lr: 1.0000e-04\n",
      "Epoch 45/50\n",
      "91/91 [==============================] - 3s 31ms/step - loss: 0.0073 - accuracy: 0.9607 - val_loss: 0.0078 - val_accuracy: 0.9602 - lr: 1.0000e-04\n",
      "Epoch 46/50\n",
      "91/91 [==============================] - 3s 34ms/step - loss: 0.0068 - accuracy: 0.9624 - val_loss: 0.0077 - val_accuracy: 0.9615 - lr: 1.0000e-04\n",
      "Epoch 47/50\n",
      "91/91 [==============================] - 3s 32ms/step - loss: 0.0066 - accuracy: 0.9656 - val_loss: 0.0077 - val_accuracy: 0.9629 - lr: 1.0000e-04\n",
      "Epoch 48/50\n",
      "91/91 [==============================] - 3s 30ms/step - loss: 0.0066 - accuracy: 0.9647 - val_loss: 0.0075 - val_accuracy: 0.9646 - lr: 1.0000e-04\n",
      "Epoch 49/50\n",
      "91/91 [==============================] - 3s 30ms/step - loss: 0.0063 - accuracy: 0.9655 - val_loss: 0.0073 - val_accuracy: 0.9629 - lr: 1.0000e-04\n",
      "Epoch 50/50\n",
      "91/91 [==============================] - 3s 30ms/step - loss: 0.0063 - accuracy: 0.9662 - val_loss: 0.0073 - val_accuracy: 0.9639 - lr: 1.0000e-04\n"
     ]
    }
   ],
   "source": [
    "# Load PTBDB\n",
    "ptb_normal = pd.read_csv(\"data\\ptbdb_normal.csv\", header=None)\n",
    "ptb_abnormal = pd.read_csv(\"data\\ptbdb_abnormal.csv\", header=None)\n",
    "\n",
    "ptb_data = pd.concat([ptb_normal, ptb_abnormal])\n",
    "ptb_data = ptb_data.sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "\n",
    "X_ptb = ptb_data.iloc[:, :-1].values\n",
    "y_ptb = ptb_data.iloc[:, -1].values\n",
    "X_ptb = np.expand_dims(X_ptb, axis=-1)\n",
    "\n",
    "# Load the pretrained model\n",
    "model = load_model(\"best_mitbih_model.h5\", custom_objects={'loss': sparse_categorical_focal_loss(alpha=0.25, gamma=2.0)})\n",
    "\n",
    "# Freeze early layers\n",
    "for layer in model.layers[:10]:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Compile with low learning rate\n",
    "opt = optimizers.Adam(learning_rate=0.0001)\n",
    "model.compile(optimizer=opt, loss=sparse_categorical_focal_loss(alpha=0.25, gamma=2.0), metrics=['accuracy'])\n",
    "\n",
    "# Fine-tune\n",
    "es_cb = EarlyStopping(monitor='val_loss', patience=6, restore_best_weights=True)\n",
    "lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)\n",
    "\n",
    "history_ptb = model.fit(X_ptb, y_ptb, validation_split=0.2, epochs=50, batch_size=128, callbacks=[es_cb, lr_reduce])\n",
    "\n",
    "# Save fine-tuned model\n",
    "model.save(\"fine_tuned_ptbdb_model.h5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d059e0e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save training history (history object)\n",
    "\n",
    "with open(\"fine_tuned_model_history.pkl\", \"wb\") as f:\n",
    "    pickle.dump(history.history, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "12d62a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model_ptbdb = load_model(\"fine_tuned_ptbdb_model.h5\", custom_objects={'loss': sparse_categorical_focal_loss(alpha=0.25, gamma=2.0)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b747063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "455/455 [==============================] - 6s 12ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluation\n",
    "y_pred_probs = model_ptbdb.predict(X_ptb)\n",
    "y_pred = np.argmax(y_pred_probs, axis=1)\n",
    "\n",
    "cm = confusion_matrix(y_ptb, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Abnormal'])\n",
    "disp.plot(cmap='Blues')\n",
    "plt.title('Confusion Matrix after Fine-Tuning on PTBDB')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ee432e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "455/455 [==============================] - 8s 18ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       0.94      0.96      0.95      4046\n",
      "    Abnormal       0.98      0.98      0.98     10506\n",
      "\n",
      "    accuracy                           0.97     14552\n",
      "   macro avg       0.96      0.97      0.96     14552\n",
      "weighted avg       0.97      0.97      0.97     14552\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_ptb, np.argmax(model_ptbdb.predict(X_ptb), axis=1), target_names=[\"Normal\", \"Abnormal\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "238e4e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training_history(history_dict):\n",
    "    # Plot loss\n",
    "    plt.figure(figsize=(10,4))\n",
    "    plt.plot(history_dict['loss'], label='Training Loss')\n",
    "    if 'val_loss' in history_dict:\n",
    "        plt.plot(history_dict['val_loss'], label='Validation Loss')\n",
    "    plt.title('Loss vs Epochs')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    # Plot accuracy\n",
    "    if 'accuracy' in history_dict:\n",
    "        plt.figure(figsize=(10,4))\n",
    "        plt.plot(history_dict['accuracy'], label='Training Accuracy')\n",
    "        if 'val_accuracy' in history_dict:\n",
    "            plt.plot(history_dict['val_accuracy'], label='Validation Accuracy')\n",
    "        plt.title('Accuracy vs Epochs')\n",
    "        plt.xlabel('Epochs')\n",
    "        plt.ylabel('Accuracy')\n",
    "        plt.legend()\n",
    "        plt.grid(True)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41b169d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dlmYIjyaHEBEREREROQkFJxERERERkZNQcBIRERERETkJBScREREREZGT8GtwWrx4Mf369SMhIQGLxcKXX3550vcsWrSItm3bEhwcTP369XnnnXfOfEFFREREROSc5tfglJmZSatWrRg/fnyp9t+2bRu9e/emS5curF69mscff5wHH3yQzz///AyXVEREREREzmV+nY68V69e9OrVq9T7v/POO9SpU4dx48YB0LRpU1asWMGrr77Ktddee4ZKKSIiIiIi57qAuo/Tzz//TM+ePYusu+KKK5g0aRIul6vEOeZzc3PJzc31vU5LSwPMueldLteZLXApFJShIpRFTo3qsHJQPVYOqsfApzqsHFSPge9cqcOyfL6ACk4pKSnExsYWWRcbG4vb7ebAgQPEx8cXe8/YsWMZPXp0sfXz5s0jNDT0jJW1rJKSkvxdBDlNqsPKQfVYOageA5/qsHJQPQa+yl6HWVlZpd43oIITFL+rr2EYJa4v8NhjjzFy5Ejf67S0NGrXrk3Pnj2JjIw8cwUtJZfLRVJSEj169Dgn7spcGakOKwfVY+Wgegx8qsPK4VyvR8MwMAwwCp771pvryF9nvi68L1DotbfQcQre4zWOHtPIf+72Gni8Bm6P+dzt9Zb83GOWxGG3EGSz4rBZcdgsBNnN50E2a/5zC3g9LFm0kO7du2O12fEYBl4DvF4Db8FzwzyvYYDHa+DJf11kyV/n9hp4vUfL6vEadKhXhehQ//4+CnqjlUZABae4uDhSUlKKrNu3bx92u52qVauW+B6n04nT6Sy23uFwVKj/kCtaeaTsVIeVg+qxclA9Bj7VYfnwFrpwNQx8zwtf/BrHXMT71uVf7RdcvHsNg1y3lxyXhxyXlxy3h1yXp+i6/NdZuS7+3GHl94XbcdhtWCwWrBYLVgtYrUef26wWLBaL7+Lf5fbi8njJ8xi4PF7chZ4fXQxfwChQUNajAcXwvS4cYLyGgddb9HMWBICCz+j2mN+Ry+stFEa8vov/wq8LjlH4XJWLHX5dcsaOPmt4J6pH+bcHWFn+nQmo4NSxY0f+97//FVk3b9482rVrp39cRURE5Li8XoOMPDdZuR6y8txk5XnyFzfZeR4y8zxkH7M+x+XNv1AudNFc5C/5Bh6veSHv9RrYrBacDhtBNitOu/mX+yCbFafj6F/yCxa71ZJ/0W1erBeUsUgrQ/5FvccwyHF5yM7zkF3oMcd19HWOy+t7Xvii3r+sfL93m78LUelYLGABrBZL/nMLNqsFu9WC3WbBbrP6njusVnNbfsuSzWr20HJ7DPJ8ITX/0W3+lvPyn5/o/FaLBVv++W35QdhiAbvVgs1qxWYFe/65CxZ7oecFr0ODAiqK+Dc4ZWRk8Oeff/peb9u2jTVr1lClShXq1KnDY489xp49e3j//fcBGDZsGOPHj2fkyJHcdddd/Pzzz0yaNInp06f76yOIiIiccwzDICPXDZhd5YtcxOVfyFktR7dZLEcDgsc42q2n4K//Beu9XoNcl4tDubDtQCaGxUae2+u7kHMVfsx/nuv2kpHrJj3HRVq2+Zie4yY9x01a/vO0HBcZue5K2BpQPgouxC35rUAWzBUFdWctVMdOhxWn3Uaww0qww5a/WAm223D6Hm0E2WDH9u3USayLxWLxBUCjoMUr//fgLRTwHDYrQXZLfvexgq5j5mu7FcK9aUR40wj1pGPYHHhsIXjsIbhtwXhsIXitwUc/DL4H3+c6+hu1YDNcODw5OLzZ2NzZODzZODxZ2AwXVpsdq81hPtodvtc2ux2b3YEtf5vNbseKF4vXjcXrBsOFxevB4nVjNdzgcWExvFi8LnOxWMFqB5sdi9UONof5aHdgsTqw2OxgdYDNhsViw+LOxurOxpKXhcWdjcWVBb4lG/IyzUdX/hgdqw0sNvPRas9/bs1/tBfabjW/HYsFLCU/N7CQ5/awZu1vtGreGDserN48LB5zwZ0D7jzw5II7f/HkgteT38TnLbpQeF2h59ZxQJOz9VM/bX4NTitWrKB79+6+1wVjkQYNGsTUqVNJTk5m586dvu316tVjzpw5PPzww7z99tskJCTw5ptvaipyERGRU2QY5l+Yc91e0nPcHMzI5WBGHvvzHw9m5HIwM48DBa8zzUf3GW3OsMOqpWfkyDarhdAgW/5iJ8RhI8xpIyTITqjDRqjTRqjDSpTdTRQZhJBLMDk4vdkEG7kEGeZzp9e86HZ4swny5mD3ZGN4vXgN79GucF5vfjjw+oJhQRc5NzZc1hDyrMG4bCG4rCG4bMHkWUNx2YJxW0Nw2UJw20Lw2IIJsRuE2C0E2zCf2yDYBk67QbDVINgOTqtBkNXAbuRic2Vhdedg9ZjBwOLOxuo6egHue8QARygEhRV/9D0PBUcYOELyL64xL7SLJBSvuVg8ALg9Hn7K2Eyn86OxWy3mRbLXU/Ri2usBI3+dKweyDkDWQXPJOABZh46uyz6cfwF+IhazvI6QomU2PJCXHzjyMs1HT94Z+X1VFhbACXQA2HUGT5SbfgYPXv78Gpy6devm6xdakqlTpxZbd8kll7Bq1aozWCoREZHyYRgGR7Jd7E/PNZeMXPalmY/703PZl57D/vRcMnM95l/iC4058f1VvtBYFE/+YI3Cg7kd+d3CHDYrjvy/1hd0C7NbLbi9Brm+8ShFHwvGo1SUlhhfq4DhJdjpKDRQ3UqIzUu0NZsYaxbR1kyiLVlEYC5Oh52goCCCgoIIDgrCGRREsNNJsDOIkGAnIfmPocFOgrw5WLIOQfahoxfpWfnPMw7BvoPmNneOv7+OgGYHugJsKecDB0dBcDR4XEdbX3whyABXprmUdqI0i61oQLQ580OdO3/xFHpewmurLb+VyHGC53ZzAfM9Hlf++13m8Tyu4s8Nb34ILAiC+SHQF25DjgZDR4gZZL2eo2G0oJyGB3NQl+do+Q1PoUFhRqEWoaLPvV4PBw4coFpsAlZHMNid5vdjDwJ7MNiC8tcFHd1ms5vhuvCCpdBrS9FtVc8rz1/HGRdYHQtFRET8xO3xkpbjJjUrj9RsF0eyXKRm55Ga5eJwlosj+etTs1ykZuVxICOP/em55HlO9lfy0jKIIpNqliNEezOIcWcQbckgigxiLBlEY742HzPzt2ViwcCDFTc2vPmPHqy4DRseixW3w4YHG26spBPGbnsi+0PqciTsPLKjGxAaXYOqYUFUC3dSNfzoY0xokK8LXkE3vGPH5xSs9xoUmQjAZrVg8+RhzfwbW9bfWDL+xpq5D0vG33iP7OXv7RuJjXRizU2HnFTIOQJZGeX0PZaB1Z5/kRqW34IRCkHhhZ4XbpEJPXpxXNDPzdciYyn+3OMyL/Dzso4+FrSG+B6zIC/D7AZltZkXmkW6YRWssxftpmUPPlqmwuUr8jr/wttiKXo+V6b5unC5XPnlcOUUavUxCs2EUPD86MW4YRhkZecQGh6OxVfWgvIWXDgX+gx2J4RWhdBq+Y9VIKxa8XW2Esa0e9xHu6+58ruv5RXq1maxlVBn+S1qtqD8OpFjeVwufp4zh969e2PVXAKAgpOIiMhxHcrM4+vf9jJr9R7W7Eo95ZaZqBAH1cODqBHuIC7CTmyYnRrhNmqE26kWYifSmkVQ9gEcOfsJyjmAI2s/9uyC5QC2rP3YsvZj8ZbjjSiPd63oXQ+ZmMs+IKw6VG8C1Rubj0GNwdYE7NXN/V3ZZneb3DRzyUkr9Do9/3UaZO6H9BTI+Nt8zEkt8fRWIB7gyHHKFxRhtjqERJuPzoj8cp+ohcBz9C/8jlAIicm/EK969IK88GNI/mNQmC6qT5Hb5eL7/IvuMz6Bl80OtkgI9v9tZqRyU3ASEREpJNftYf6mfXyxeg+Lfk+hppFCQ8tuBlsPEEou0Y48YuwuIm15RFrzCLPmEWbJJYRcnPnjXxzeHKyGG6vhwWKYg8VJd0M6kHyaBXRGmRf4ITFHH0NizIv9YutizL/mn6y7UcGSuR/2/w77N5uPqTvNdZn7YfsxUxI7wvIHg7tP/bPYgiA8DiJiITwWIuLwhFZn3dYUWrTvgj2sSqGQFA3OSPMiWUTED/Svj4iIVBger0FqVh6Hs/I4nOXiUGYeeW4v0aEOokIcRIcEERXqIMJpx2otv5YAw+Nm3Ya1rF25jNQdv1Hbs5OHLXsY79iL01JCK487fykvQeFmcAivYS5hNYq+Ds9/HVbd7NJ0tuRlwoE/zCC1b9PRQHV4u9klysdihprgSLMFyJn/6HsdYXa3iojzBSTCY/ODXdF69Lpc7EibQ/OmvUHdg0SkAlFwEhEJVKk7YdPX5uDa+NbmX+39zOM1p6nOzF/SCz3PyPXkP7o5km2GosOZRUNSWo6rVN3hrBaIDHEQHWIGqqjQIKJDHEQ4bezfY2Xvj9uJCXMS7TSoYs0m2pJJJOmEezMI9aZjzUmFrENkpGwha896ojK3cz4uzi84ga3QyewhUL0RVKlvBoAi411KmoEs/9EWdHQ8im+xHfNoPzpguiIKCoOEC8ylMFc2HNljfg8F34nV6p8yioicJQpOIiKByDDg86Gw65ej6yISIKG1GaIKHvPD1P70XNbuSiUzz7yXjYHhG9Rv3u3eHNhPoW0uj5ecnBzc2Ucg+zBGzhGsOUew5h7B4UrDnpdGkDudEE86Tk8m5N/LxIYXOx5seLBbvATjIQwv8b71XvYaVfnNqM9B73ns8NbnIFFFPl5UiIOYUAcxYUE4bFbSsl0cyV+y8jx4DUjNcnEkK5f6lmTqWrbR0rqNxpZdVLWkE7XQnBgh3HLimdHC8xeAHMPBgZC6BMU3p1q9Vlhjm5rjeqITzaAjRzlCoFoDf5dCROSsUnASEQlEO382Q5MtCGLqmd2p0vfC5r2weY5vt1R7dTYY9fglpw7rjbq4sRFJFhGWLCLJItKSeczrLCLJJMKSTRSZhFpyS18mC0Vbak6gCbu4lDW+17lhNXHFtcZSsw3Bie2x1byg+EBvrxcO/YVr9ypcu1bC3jU4D2zA5jrxbGteLGQQSqoRzmEjjCNGGEcwH5OphiOuGc0v6MDFbdtSKzio9J9XRETOKQpOIiKB6Mdx5mPrW3D3foPNO1PYvmEZWduXE3ZwPQ09f3GeZS/R7v10Zj+dHb+e1ulybGHk2SNwOSJxB0XiCYrCCI6C4EgsITFYgyNxOoPz76XjwGEPKt4lzTdlsgUO/Al7V8GeVXDgD5yZe3D+tQf++ib/jBao1ghqtjHHwSSvheTfIC8dB1Bk5Is9BOJaQsIFuGs059dNO7mwa0/s4dUgJAZrcBSRVhuRQLzHS1q2i7QcN2nZLq6IDqF6xFkcMyQiIgFLwUlEJND8vRG2zMXAwr/2XMK3o+eRmefBvM/7xcDFBNmsdKgZxJXV9nGhcyeJuVsIOrDRHE8THJl/E8n8xXnM6yLbo8EZSbDNTnB5fobzLj36PCcNkteYIaogTB3ZBQc2m0thhUKSrztitUa+mdYMl4v9e+ZgJLQpcWIBh81K1XAnVcMVlkREpGwUnEREAkRqVh7frU+h5oIn6QLM8bTns+1OwENEsJ12iTG0q1uFC+tVoWXNKIIdATIuJzgS6nU1lwIZ+4+GqJwjEH9+sZAkIiJyNun/PiIiFVhGrpvvN/7N7LV7WfzHfmp497PIuRAssKDaLTzZpimdG1SjUWwEtnKcntvvwqtDoyvMRUREpAJQcBKRyun3ObD4ZbjkUWjcy9+lKZMcl4eFm/fxv7XJ/PD73+S4vL5t/4r6AUeuh5xaF/Pq0Dv8WEoREZFzi4KTiFQ+v38Dnw4Erxu+uBuG/wxRtfxdquNye7z8npLOiu2HWL7jMIs27ycj9+jdVetVC6NfqwQGNA6m/gd3ARDc7WF/FVdEROScpOAkIpXL5u/g00FmaHKEQm4afDkcbv+ywtygMyPXzeqdh1mx/TArdxxm9c7D+ZM7HJUQFUy/Vgn0a5VA84RILBYLLHoZXJnm5AjnXean0ouIiJybFJxEpPLY8j18ejt4XdD8Gug2Cv5zCWxbBMvfgw53n9XieLwGmXluDmfmsWZXKit3mGHp95Q0vEbRfSOC7bSpE0O7xBg6NajKBbVjsBYes5SXBb+8Yz7vPMKc0ltERETOGgUnEakc/poPn9wCnjxoehVc81+wOaDHc/DtvyDpaXMK7GoNTus0hmGwbOshfvrrAOk5btJz3GTkusjIdZOR4yY9101m/nOzFcmguWU7fxi1cRX6J7d2lRDaJVahbWIM7erG0KhGRNGgdKw1H0HWQYhOhGYDTusziIiISNkpOIlI4Nu6CKbfDJ5caNIXrptshiaA9kPh96/NVqcvh8Ed353SdNZer8EPv+9jwsI/Wb0ztdTvG277ikccn7LZ3pjPW75D6/rxtEuMoUZkGe6K5HHDT2+azzs9oOm4RURE/ED/9xWRk3Nlmy05wVH+Lklx23+E6TeBOwca9YLrphwNTWCOaxowASZ0gt3L4af/gy7/KPXh3R4v36xLZsKCv9j8dzoAQXYrfVvGkxAdQniwnXCnnYj8xzDn0ddRR34n6sNZ4IXG7s08njsOmk8t+1irjV9C6k4IrQqtby3be0VERKRcKDiJVBZeL2Tug4i48j3uliRzcoW8DOj1MlxwW8UZX7PjZ/joBnBlQcOecMM0sAcV3y+qFvR6yWxxWjDW3Deu5QkPnePy8Pmq3fxn0VZ2HsoCINxp57aLEhlycV1qRJykxcidB58+ZI63qtkOktfCxq9g/vNw+TOl/4yGAT+OM593GAZBoaV/r4iIiJQbBSeRyiDzgDn99o6l0OJauGIsRMSe3jFd2ZD0DPz6n6PrZt8PWxdA3zfKp/XJ64Xti8Fqh1oXFgk9hmGQlmNOrHAoK898zMzD4zWIiwrmvJyN1PrmFiyuTHPs0g0fgN15/HO1usnssvf71/DFPXD3ghL3z8h1M/Pnnby3ZBv70nMBqBIWxJDOdbm9Y12iQhzF3lOixa/A3+sgpArcPB3+/MEMbj++DlXPMwNoafz1g3kcR5jZ7VBERET8QsFJJNClrDfH9xzZab5e/7k5u9zlz0DbO05tCu6U9fD5UNi/yXzd4V4Irw7z/20ef/cKs0tcrbanXu6dv+D59hFsyWsAyLUEsyGoJb/SkoXu5izPjsfjLfmtrS1/8kHQWCyWbJbRkn/vH07VD38jITqEhKhg4qNCiI8OJi4yGIfN/PwWC1i7jCV2x8/Y9m0g/bvnyejyhLkNC5k5uXy7y8rTry3mSLZ5D6X4qGDu7lqfG9vXJjSoDP9c7l0NS14zn/d9HcJrQOub4dBfZqD630MQVRvqX3LyYxW0NrUdBKFVSl8GERERKVcKTiKB7Pdv4PO7zHv7VKkPlz8LS16H5DXwzUhYOx36/R/ENi/d8bxec8rr758xxzSF1YABE6Hh5eb2ul3h8yGQugMm94TLnoaOD5QtnB3ZjZH0DJb1n2ED0o0QcnBQnTTa5C6nDcsZBux3RLHU25zlllZsDG2DJzyB6NAg6udt5p8pLxFONj97mnGHayQ5OXmwb3+pTn+FdSD/CXqD0OXjGbS0KquMRoW2WgE39auFMeyS8xhwQU2C7GUMnu5cmHUvGB5ofrW5FOj+BBzaaobPT2+HO7+H6o2Of6w9K2H7ErNF7qLhZSuHiIiIlCsFJ5FAZBhmi8b8583X9S6B66eaLRJN+pr3LPrhOXMyhHe6QKf74ZJHISgMgJQjOXy5Zg/hTjuta0fTOC4CR+bf8OW9Zlc8MCda6D8ewqodPW/t9nDPErPFZOOX5hTfWxfB1e+YrSonkpcFS/8P79JxWN05eA0LMzzd+ChsIJe1bcZ57OS8tBUkHPqZqH3Lqe4+wgDbTwzgJ8gBwhpCja6w/jMgE+p0ouX10/lflo29R3JITs1m75Ec9qZmk3wkm+TUHPal5+LxGhgYvq9tIR2Y5e3K1dbFvBE0kavcL5GFE8OA+BAv/+rbij6tamE70dTgJ7LgBbOlLqw69H6t6DaLBfpPgCO7Ydcv8PH1MPSHot9xYQWtTS2ug+jap1YeERERKRcKTiKBxpUNsx+AdTPN1xfeDVe8cHQmOasNOtxjBqjvHoVN/4Ol/wcbZrG/6wv83866fLp8N3mF+sH1dqzkRfu7RBppuG3BpHV5lpiu92ApqSUpJNoMaaumwbejzDE4EzvDNf8xxxodyzBg/ecY857Ckr4XK/CLtwkveAZycdfLmNm9ISFBNqAJ0NN8jzvXDH1/LYCtC2HvKji4xVwAaneAWz8l3BlBwwhoGBtRtu8wuyNM7ERi2h7WdvoR+ryGy+Vizpw59GoRd+qhadfyo9OG9/s/CKtafB9HMNz0Mbx7KRzebt57auBsc31hB/406w6g80OnVh4REREpNwpOIsfyeswxKhHxEFXT36UpKi3ZvNDeu8rsvtX7FWg3pOR9o2rCjR/C5m9xf/0P7Kk7qT77Ni7ydGCeZyB1EusTbc/jij1vcT3fgwEbvIk8mHs/f31Xk5jF39OqdjStakXTuk40rWtFExOWP3mDxQJtB0Pti+CzO2DfRvjgaug8Ai598miI27MSvnsMdv2CBdhtVOPfrltJq9eL1/q3pEGN8JLLbndC3YvN5bKnIDvV7LK2dSF43dDjeXCWMSwVFhIN/d+GDwaYrXONe0Ni11M/HpiB9st7wfDC+TdCkz7H3zesGtw6E97rYbY8fXUfXPte0dkKf3oTMKDhFRDb7PTKJiIiIqdNwUkEzBuM7lhqThe96X/mtN6h1eDen05/drrysmclfHIrpCdDSIw5i1y9Lid8yx9/p/P26jh+OPA8D9o+Z4jtW/rafuHK4A3Ym42A32YAZivOHw3u4IvwQUTsySJobxqHs1ws3LyfhZuPjh2qVy2MC2pHc0FiDG3qRNM4thH2u+bD3MdhxWRYOs68r9KVY2HFFFj7MQCZhpMJ7v58FTKAR665gH7nx2Mpy5TmIdHQtJ+5lJfzuputdb/+F766H+5afHrHmz/GbBELjzOnPj+Z6o3hxg/gw2vM7odV6sOl5mQVpKeY49MALh5xeuUSERGRcqHgJOcuj8tsxdj4FWz6GrIOFN2edcAcy3PzdP/ft2jdZ2arhDsHqjc1y1Sl3nF3X7/nCOPn/8l3G1Ly1wTza8OH6Xr+vTRZ/jT2vatgwRhzU0Q8XP0Ojep346n8vXPdHn5PTmft7lTW7Exlza5Uth7IZFv+8sXqPQCEBtk4v1YUF9S5hys7XUDLlU9i3bMCJvXwleVzTxdecd9Er04XMKdHIyKDSzmd99lw+WhzmvBDf2Gb9xg4TjGY7fgJfn7bfH7VW2awLY36l0DfceY074tfNsNT65vNCTo8eeYU7XU6nlqZREREpFwpOElgSks2Wzl2Lzdv+BpVK3+pU+h5LfMCtnDocefBtkXmxAa/fwPZh49uC4kxxwU1G2COTZnUE/74FlZ/CG1uP9uf0OT1mgEnf2pro+EVeK7+LzgjMTxeDAMMDAxz7gM27E3j7QV/Mv/3fYD50Xu1iOO+7g1onpB/36VW35utQ4tfgcTO0Oe1YtNcO+02s5te7WgG5l+3p2blsXpXKqt3prJ652HW7EwlPdfNsq2HWLb1EBOpSk2e453QibT0/s4qbwNGuwZiq92OSQNaHD1/RRIUClf/Byb3xLp+Jgl1Y4HeZTtGXqZ5g2AM895MjXqW7f1tbjenKf/xDXPsWkgMLJ9sbrt4hP9Du4iIiAAKThJoDANWfwBzn4TcI+a6I7vMAFWSoPCjISoozBwjk3Pk6PbQamb3r2b9zfE0tkKtId2fMKfl/u4xqNcVYhLL7WPkuj0cyMhjf3pukeVw2hHsh/4gKm0L1bK20sy1nlaWPwGY6O7HK+tuxLtu6UmPb7VA/9Y1Gd7tvOITJ1htcOFd5lIG0aFBdG9cg+6NzdnzvF6DP/dnsGrHYVbvTGXVzsNs2Qf9s56kriWFw8F1GNWvKde3rY31VCdbOBtqtzfHZv34Ou23j8c7fQN0GAYNe5jf1cl8/ywc3gaRtcxJOk7FpU+b05Rv/Aqm32iuq9bYnNlQREREKgQFJwkch7ebXee2LjRfJ7Qxx4TkZZnTOx/Zbd4EtuB55n7Iy4D9v5tLgfDYo2GpTiewHec/g04PwOZvYdcys0Vh0P9O7Way+TbsPcKM5buYsy6FQxnZJFr+prFlF02sO2lk2U0Hyy7qWlKwWYyjb7JArmFnlOsuZnlPPJ4JwGGzcM0Ftbi323nUrRZ2ymUtDavVQqPYCBrFRnDThXUAOJLtYu2uVFLScujRNPboZBIVXbfH8B7egWXDF1i3LjCnZI+pC+3vMluRQqJLft/WReYYKYD+b0HwKbaqWa1my9eR3eZYNoDOD57W701ERETKl4KTVHxeD/z6LvwwGlxZYA82W4MuGn780APmLGdpeyE1P0xlHTCnsa7doXQtCVYbXD0RJl4MO340x510LNtNSI9ku5i9di8zlu9k/Z40mlm2M9ExlZbObQRbXCW+J8cRTWZ0IzzVmmKLa47lvEt4MroeT1ksWDjac8uCBSzm64L2nCC7Fae9FJ/tDIkKcdC1UXW/nf+U2YPwDPgPC41OXBq5Fduaj8ygPu8JWPBvc5a8DvdAjaZH35Obbk4qAebMhiVNxV4WjhC4+ROY2hfsQdDyhtM7noiIiJQrBSep2Pb/YQ6c3/WL+Tqxszn4vup5J3+vI8TcrzT7Hk+V+nDFGPj6YbNL1nmXQo0mJ3yLYRj8uu2Q2bq0Ppkcl3m/pDb2rXwY9CKh3gxzP3sIlhpNoEZz84I8thnUaE5weA2CNa7FL7Kc1fFeNgjbpU/Cuk/hl//Cvg2wcoq51O1iBqjGvWHek2YLZ3Qd6PFc+RQgvAYMX5afhvUbEBERqUgUnKRi8rjM+9gsfAk8uRAUAT1GQ9s7zn73pbZ3mBNJ/Pk9zLoHhn5fdCxUvv3puXz1205mrtjF1gOZvvWNYsN5oOEh+vz2Eta8DLPFq/8ELFXqla7lS86+oFDzPlVtBpnT1P/yH/j9a3MWxu1LICIB0vea+/afcHr3lDqWuueJiIhUSApOUvEkrzW7QKX8Zr5u0AP6jTMneDgL9qXnMGvVHpKP5OR3g7MQGfoA99h+ISR5DUsmj+LHWkOxYMFiAcPrZenvVkb+shiP1xyfFBZko1+rBG5sX5vWnvVYPr4PXJmQeDHcMgOcx7nxq1QsFsvRG/Gm7oIVk2DltKOhqcOwk95LS0RERCoHBSc5+1zZkJ0KOanFHw/+ad441fCY0zJf+aI5vuQMd1syDINlWw/x4bIdzN2QgttrFNvnL+sg3goaT8fdk3l5a13WGfULbbUCBm3qRHNT+zr0OT+eMKcd/loA028GdzbU7wY3TTdbMyTwRNeGy5+FSx6F9V+Y4+Y6P+jvUomIiMhZouAk5c/rhYNbzNnB9qyEvzdA1qGjAcmTe/JjNOsPvV81x3ycQWk5Lr5YuZsPf9nJn/syfOvbJsbQoZ55byNv/r2SMOqz8c9NNDv0A5Oj3uO9ZlNwWYLxeD2k7NrOQ1dfTLOahW58uiUJPrnV/LwNesCNH4Ij+Ix+HjkLHCFwwa3+LoWIiIicZQpOcnoMw5y5riAk7VkJe9dAXvqJ32exQnC0Oc3zsY8Nr4DGV57RYq/fc4QPl+3gqzV7yXZ5AAgNsnH1BTW57aJEmsZHlvzGrPdgwkVUz9jOY0GfwZUv4HK5mDNnKw1rFOp+9/scmDkIPHnQuA9cPwXszjP6mURERETkzFFwkrLb/wdsmg17VplBKSOl+D6OUEi4AGq2gfjWEFbdDEUhMWZAckac9VnDclwevv4tmQ+X7WDNrlTf+kax4dx+USIDLqhJRHDxSR+KCK0CV42Hj6+HZW9D415Q66Ki+2z4Ej6/E7xuaDYArn2vxMkkRERERCRwKDhJ2XjcMLW3eXPZAhabOZV2zbZHl2qNT3yPpTMox+Vh16Esth7IZPuBTLYfzGTbgUw27k0jLccNmDeKvbJFPLdflEj7ujFYyhLiGvU0Z1tbNc28Me5di45u+22mOfOe4THvwzNgot++BxEREREpP7qik7JJWWuGpqBw6P64GZLizvfLhAeHMvNYs+sw2w5k+QLS1v2Z7D2SjVF8bgcAakaHcEuHOtzQrjbVI06j69wV/4atCyF1B7akJ8F6BZa10+HrBwEDWt9q3m9K042LiIiIVAoKTlI225aYj3W7QMf7zvrpD2Tk8t36FL5dn8yyrYd8038fK8Jpp261MOpWC6NetTDqVQulXrVwWtaMwmYthy6Czgi4+h2Y0hvr2o9oXXU39tX5LU9t74A+r+t+PCIiIiKViIKTlM32/OBUr+tZO+W+9Bzmrk9hzroUftl2kMJZqWGNcBrGhlO3auGQFEbVsKCydb87FYmdoNP98NNbJB7MD00X3gO9Xjrr47dERERE5MxScJLS87hgx8/m8zN80899aTl8uz6Fb9Yls3z7oSJd786vFUXvlvH0bhFPnap+vidS9ycx/vwBy76NeC66D9sV/1ZoEhEREamEFJyk9PasAlcmhFSBGs3L/fC5bg+frtjN7DV7WLHjcJGw1Kp2NH1axtGrRTy1q1SgG8g6gnEPmsPS2dPofOlwbApNIiIiIpWSgpOU3vbF5mPdi8t1/I5hGPywaR9jvtnI9oNZvvUX1ImmT8t4rmwRR62YChSWjhUUzpHQemppEhEREanEFJyk9LaV//imLX+n89zXG1my5QAA1SOc3NO1Pr1bxpMQHVJu5xEREREROR0KTlI67lzY9Yv5vO7pj29Kzcpj3Pdb+GDZDjxegyCblTu71OO+7g0Id+pnKSIiIiIVi65QpXR2Lwd3DoTVgOqNT/kwbo+X6b/u5LWkP0jNcgHQs1ksT/RpSmLVsPIqrYiIiIhIuVJwktLxddPrcspjeX768wCj/7eRzX+nA9AoNpyn+zbn4obVyquUIiIiIiJnhIKTlM62gokhyt5Nb+fBLP49ZyNzN/wNQFSIg3/0bMQtF9bBbtNNYkVERESk4lNwkpPLyzK76kGpJ4bYfTiLxX8cYOHmfSzcvJ88jxeb1cJtHeow4vJGxIQFncECi4iIiIiULwUnObldv4DXBZE1oUr9EnfJcXlYvv0QCzfvZ9Ef+/lzX0aR7Z0bVOXpvs1pHBdxNkosIiIiIlKuFJzk5Ap30ys0vmn7gUwWbt7Hoj/28/PWg+S4vL5tVgtcUCeGbo2qc0nj6rSsGYVF9zkSERERkQDl9+A0YcIEXnnlFZKTk2nevDnjxo2jS5fjj6P56KOPePnll9myZQtRUVFceeWVvPrqq1StWvUslvocs/3o/Zty3R7eWbiVL1bvZkehm9UCxEY6uaRRdS5pVIOLG1QjKtThh8KKiIiIiJQ/vwanGTNmMGLECCZMmEDnzp35z3/+Q69evdi4cSN16tQptv+PP/7IwIEDeeONN+jXrx979uxh2LBhDB06lFmzZvnhE5wDctNhzyoANgW34qG3fuSPv81ueA6bhXaJVbikcXUuaVSdJnERalUSERERkUrJr8Hp9ddf584772To0KEAjBs3jrlz5zJx4kTGjh1bbP9ly5ZRt25dHnzwQQDq1avHPffcw8svv3xWyx0Q1n0GP78N17wL1Rqc+nF2/AyGh1RnAn0/2InHa1AtPIjHezelZ/M43axWRERERM4JfrvqzcvLY+XKlYwaNarI+p49e/LTTz+V+J5OnTrxxBNPMGfOHHr16sW+ffv47LPP6NOnz3HPk5ubS25uru91WloaAC6XC5fLVQ6f5PQUlKFcy2IY2L8fjeXITryLXsJz1YRTPtShtXOJBb7LbITHa9CnZRxP92lClbAgwKgQ36G/nZE6lLNO9Vg5qB4Dn+qwclA9Br5zpQ7L8vkshmEYZ7Asx7V3715q1qzJ0qVL6dSpk2/9Cy+8wLRp09i8eXOJ7/vss8+44447yMnJwe12c9VVV/HZZ5/hcJQ8nubZZ59l9OjRxdZ//PHHhIaGls+HqWBiMrfQ9Y/nAfBY7MxrPo48R2SZjuH2wne7rTy4/ylaWrfxqHc4YXU70qqqX34uIiIiIiLlLisri1tuuYUjR44QGXni62W/97M6dkyMYRjHHSezceNGHnzwQZ5++mmuuOIKkpOT+de//sWwYcOYNGlSie957LHHGDlypO91WloatWvXpmfPnif9cs4Gl8tFUlISPXr0OG74Kyvr3KOteDbDTc9qKXg731Tq96/fk8ajX6wnZV8KzZzbAfjn3UOJji0+7kzOTB3K2ad6rBxUj4FPdVg5qB4D37lShwW90UrDb8GpWrVq2Gw2UlJSiqzft28fsbGxJb5n7NixdO7cmX/9618AnH/++YSFhdGlSxfGjBlDfHx8sfc4nU6cTmex9Q6Ho0L9CMqtPB43bPrKfN7iWlj/ObZVU7F1eRhsJ67uXLeHt374k4mL/sLjNbgm9E9sXgOqNqR6rfNOv2yVXEX7TcmpUT1WDqrHwKc6rBxUj4GvstdhWT6b9QyW44SCgoJo27YtSUlJRdYnJSUV6bpXWFZWFlZr0SLbbDbAbKkSYNsiyNwPoVWh35vmY9pu+OO7E75t3e4jXPXWUsYv+NMcy3R+PP9unWpurHf86eFFRERERM4FfgtOACNHjuS9995j8uTJbNq0iYcffpidO3cybNgwwOxmN3DgQN/+/fr144svvmDixIls3bqVpUuX8uCDD3LhhReSkJDgr49Rsaz7zHxsfjU4w6FN/vf363+P+5ZPV+zi6glL2fx3OlXDgphwaxvevqUNIXvyJ+moq+AkIiIiIuc2v45xuvHGGzl48CDPPfccycnJtGjRgjlz5pCYmAhAcnIyO3fu9O0/ePBg0tPTGT9+PP/4xz+Ijo7m0ksv5aWXXvLXR6hYXNmw6X/m85bXm4/thsDS/zNbovZvhuqNfbsbhsHERX/x8nfmRBxXNo/j31e3oGq4EzIPwN/rzR0VnERERETkHOf3ySGGDx/O8OHDS9w2derUYuseeOABHnjggTNcqgD1x1zIS4eoOlDrQnNddB1o1As2fwPL34PerwDg9Ro89/VGpv60HYBhl5zHo1c2Pjoxx/YfzccazSC8+ln+ICIiIiIiFYtfu+pJOVs303xscQ0UHgt24V3m45rpkJtOrtvDQzPW+ELTU32bMapXk6KzGW5fYj6qtUlERERExP8tTlJOslNhS/5EGwXd9ArU7wZVG8LBLeSs/JihG1vz458HsFstvHZDK/q3rln8eNsWm4/1up7JUouIiIiIBAS1OFUWv38Nnlyo3hRimxfdZrH4Wp32/TCeH//cT2iQjcmD25ccmtJT4MAfgAXqdj7zZRcRERERqeAUnCqLgm56La8zg9IxdtXuTzbB1PHspGfoFqbfdRFdGx1n7FLB+Ka4lhASc4YKLCIiIiISOBScKoP0v492rWt5XbHN6/cc4erJ6/nMfTEAb9T9lVa1o49/vG2LzEd10xMRERERARScKocNs8DwQq32EFO3yKaf/jzATf9dxoGMXH6scjUAYdvmwpHdxz/etvyJIRScREREREQABafKwddNr+ikEF//tpfBU5aTkeumQ70qvDL8RnOWPMMDK6aUfKzUXXB4G1hsUKfjGS64iIiIiEhgUHAKdIe2wp4VYLFC86t9q7/+bS8PTF9NnsdLrxZxTBtyIZHBDmg/1Nxh1TRw5xY/XsE05AkXQHDkWfgAIiIiIiIVn4JToFv3uflYvxuE1/CtnrJ0O4YBN7Srxfhb2hDssJkbmvSBiATI3A8bvyp+PF83Pd2/SURERESkgIJTIDOMQje9PTophMvjZf2eIwDcc8l52KyFZtmzOaDdHebzX98tfjzd+FZEREREpBgFp0D293o4sBlsTmja17d6c0o6uW4vEcF26lUNK/6+NoPA6oDdv8LeNUfXH94OR3aZ2+pcdMaLLyIiIiISKBScAllBa1OjKyA4yrd67e5UAFrVisZqLX5PJyJioVl/8/nyQq1OBVOa12oHQSUELhERERGRc5SCU6Dyeo+ObzpmNr3fdpnd9M6vFXXsu4668G7zcd1nkHXIfK5ueiIiIiIiJVJwClS7lkHabnBGQsOeRTb5WpxOdJPb2hdCXEtw58DqD83xTZoYQkRERESkRApOgWrdZ+Zj037gCPatzsx188ff6QC0PlFwsliOtjotfw/2b4aMFHO8VK0Lz1ChRUREREQCk4JTIPK4YMMs83nL64psWr/nCF4D4iKDiY0MLuHNhbS4DoKjIXUHJD1trqt9YZEgJiIiIiIiCk6B6a8FkH0IwmpA3a5FNv22uxTjmwoEhcIFt5nPt8w1H+t1Pf7+IiIiIiLnKAWnQOS7d9M1YLMX2bSmNOObCmt/J1Bo5j0FJxERERGRYhScAk1eFvz+jfn8mNn0ANbuSgVOMr6psCr1oWEP87kjFBLanH4ZRUREREQqGQWnQPPHt+DKhJi6ULNtkU0HM3LZfTgbgJal6apXoNMDYLFC495gDyrHwoqIiIiIVA72k+8iFUrBbHotrjNnxiukYHxT/ephRAY7Sn/Mel3hwdUQHltepRQRERERqVQUnAJJ1iHYkmQ+L6Gb3pqCbnq1ost+7Ji6p1wsEREREZHKTl31Asmm2eB1QWxLqNGk2OZS3fhWRERERETKTMEpkBR002t5bbFNhmH4JoZQcBIRERERKV8KToEiLxO2/2g+b35Nsc27D2dzOMuFw2ahaXzEWS6ciIiIiEjlpuAUKLIOAgbYnBBdp9jmgvFNTeMjcdptZ7dsIiIiIiKVnIJToMg+bD6GxBSbTQ+O3r+p1alMDCEiIiIiIiek4BQofMEpusTNmhhCREREROTMUXAKFIVbnI7h9nhZvycNgFZlufGtiIiIiIiUioJToMhONR9LCE5b9mWQ7fIQ7rRTv3r42S2XiIiIiMg5QMEpUJygxalgfFPLmlHYrMXHP4mIiIiIyOlRcAoUJwpOGt8kIiIiInJGKTgFihNMDrF21xFA45tERERERM4UBadAcZwWp+w8D5v/TgfU4iQiIiIicqYoOAWK40wOsWHvETxeg+oRTuKjgs9+uUREREREzgEKToHiOC1Oawrd+NZSwo1xRURERETk9Ck4BYqC4BQcXWT1b7vN8U2ta2t8k4iIiIjImaLgFChyUs3HY1qcCmbUO79W9FktjoiIiIjIuUTBKRC4csCVZT4vFJwOZ+ax46C5/nzNqCciIiIicsYoOAWCgtYmixWckb7VBa1N9aqFER0adPbLJSIiIiJyjlBwCgSFxzdZj1ZZwfgm3b9JREREROTMUnAKBMeZUW9t/ox6Gt8kIiIiInJmKTgFghKCk2EYvq56uvGtiIiIiMiZVebgVLduXZ577jl27tx5JsojJSkhOO1JzeZARh52q4XmCZHHeaOIiIiIiJSHMgenf/zjH3z11VfUr1+fHj168Mknn5Cbm3smyiYFfMEp2reqYHxTk/gIgh02PxRKREREROTcUebg9MADD7By5UpWrlxJs2bNePDBB4mPj+f+++9n1apVZ6KMkp1qPhZqcdL4JhERERGRs+eUxzi1atWK//u//2PPnj0888wzvPfee7Rv355WrVoxefJkDMMoz3Ke20roqrcmPzi1VnASERERETnj7Kf6RpfLxaxZs5gyZQpJSUlcdNFF3Hnnnezdu5cnnniC77//no8//rg8y3ruOiY4ebwG6/bkT0WuiSFERERERM64MgenVatWMWXKFKZPn47NZuP222/njTfeoEmTJr59evbsSdeuXcu1oOe0Y4LTX/szyMrzEBpko0GNcD8WTERERETk3FDm4NS+fXt69OjBxIkTGTBgAA6Ho9g+zZo146abbiqXAgrFglNBN70WNaOwWS1+KpSIiIiIyLmjzMFp69atJCYmnnCfsLAwpkyZcsqFkmMcE5wKJoZorW56IiIiIiJnRZknh9i3bx+//PJLsfW//PILK1asKJdCyTGOmVXPd+NbTQwhIiIiInJWlDk43XfffezatavY+j179nDfffeVS6GkEI8bcs2JIAiJIcfl4ffkdABa1Y7yY8FERERERM4dZQ5OGzdupE2bNsXWX3DBBWzcuLHMBZgwYQL16tUjODiYtm3bsmTJkhPun5ubyxNPPEFiYiJOp5PzzjuPyZMnl/m8ASPnyNHnwVFsTE7D7TWoGhZEzegQ/5VLREREROQcUuYxTk6nk7///pv69esXWZ+cnIzdXrbDzZgxgxEjRjBhwgQ6d+7Mf/7zH3r16sXGjRupU6dOie+54YYb+Pvvv5k0aRINGjRg3759uN3usn6MwJGTaj4GRYDN4Rvf1Kp2NBaLJoYQERERETkbyhycevTowWOPPcZXX31FVJTZVSw1NZXHH3+cHj16lOlYr7/+OnfeeSdDhw4FYNy4ccydO5eJEycyduzYYvt/9913LFq0iK1bt1KlShUA6tatW9aPEFiOMzGExjeJiIiIiJw9ZQ5Or732Gl27diUxMZELLrgAgDVr1hAbG8sHH3xQ6uPk5eWxcuVKRo0aVWR9z549+emnn0p8z+zZs2nXrh0vv/wyH3zwAWFhYVx11VU8//zzhISU3G0tNzeX3Nxc3+u0tDTAvIGvy+UqdXnPlIIyHK8slvT92AEjOAq3y+ULTi0SwitE+eXkdSiBQfVYOageA5/qsHJQPQa+c6UOy/L5yhycatasyW+//cZHH33E2rVrCQkJ4Y477uDmm28u8Z5Ox3PgwAE8Hg+xsbFF1sfGxpKSklLie7Zu3cqPP/5IcHAws2bN4sCBAwwfPpxDhw4dd5zT2LFjGT16dLH18+bNIzQ0tNTlPdOSkpJKXF/r0E+0BQ5kevh+9hy2HTSrLGXjr8zZchYLKCd1vDqUwKJ6rBxUj4FPdVg5qB4DX2Wvw6ysrFLvW+bgBOZ9mu6+++5TeWsxx47TMQzjuGN3vF4vFouFjz76yNdN8PXXX+e6667j7bffLrHV6bHHHmPkyJG+12lpadSuXZuePXsSGRlZLp/hdLhcLpKSkujRo0eJwdO6fA/sgKq1GxLXrAMsX0ntmBBu6N/FD6WVkpysDiUwqB4rB9Vj4FMdVg6qx8B3rtRhQW+00jil4ATm7Ho7d+4kLy+vyPqrrrqqVO+vVq0aNputWOvSvn37irVCFYiPj6dmzZq+0ATQtGlTDMNg9+7dNGzYsNh7nE4nTqez2HqHw1GhfgTHLU+eWZnW0CpsyJ+GvHWdmApVdjFVtN+UnBrVY+Wgegx8qsPKQfUY+Cp7HZbls5U5OG3dupWrr76adevWYbFYMAwDONpy5PF4SnWcoKAg2rZtS1JSEldffbVvfVJSEv379y/xPZ07d2bmzJlkZGQQHh4OwB9//IHVaqVWrVpl/SiBodDkEGt3m1OTt6ql+zeJiIiIiJxNZb6P00MPPUS9evX4+++/CQ0NZcOGDSxevJh27dqxcOHCMh1r5MiRvPfee0yePJlNmzbx8MMPs3PnToYNGwaY3ewGDhzo2/+WW26hatWq3HHHHWzcuJHFixfzr3/9iyFDhhx3coiAlx+cjOBo1hSailxERERERM6eMrc4/fzzz8yfP5/q1atjtVqxWq1cfPHFjB07lgcffJDVq1eX+lg33ngjBw8e5LnnniM5OZkWLVowZ84cEhMTAfPeUDt37vTtHx4eTlJSEg888ADt2rWjatWq3HDDDYwZM6asHyNwZKeaD/ZI9qebswM2T/D/2CwRERERkXNJmYOTx+PxdZOrVq0ae/fupXHjxiQmJrJ58+YyF2D48OEMHz68xG1Tp04ttq5JkyaVfnaPIvJbnNItEQCEBdkIDTrloWkiIiIiInIKynwF3qJFC3777Tfq169Phw4dePnllwkKCuK///0v9evXPxNlPLflB6cjhAFeokOD/FseEREREZFzUJmD05NPPklmZiYAY8aMoW/fvnTp0oWqVasyY8aMci/gOS8/OB3yhgHpVAlTcBIREREROdvKHJyuuOIK3/P69euzceNGDh06RExMzHHvvySnyDB8wemAxwxO0aGVdzpIEREREZGKqkyz6rndbux2O+vXry+yvkqVKgpNZ0JuOhjm9O77XOasgWpxEhERERE5+8oUnOx2O4mJiaW+V5OcpoJ7ONmDOZBrVlWMxjiJiIiIiJx1Zb6P05NPPsljjz3GoUOHzkR5pLCC4BQczeGsPEDBSURERETEH8o8xunNN9/kzz//JCEhgcTERMLCwopsX7VqVbkV7pyXk2o+hsRwONMFQEyYxjiJiIiIiJxtZQ5OAwYMOAPFkBIVtDiFxHBILU4iIiIiIn5T5uD0zDPPnIlySEkKBafUIwpOIiIiIiL+UuYxTnIWFW5xUlc9ERERERG/KXOLk9VqPeHU45pxrxzlBycjJJpUddUTEREREfGbMgenWbNmFXntcrlYvXo106ZNY/To0eVWMMEXnHIdUbi9BqDgJCIiIiLiD2UOTv379y+27rrrrqN58+bMmDGDO++8s1wKJkB2KgBZ1ggAgh1WQoJsfiyQiIiIiMi5qdzGOHXo0IHvv/++vA4n4GtxSrOYwamKWptERERERPyiXIJTdnY2b731FrVq1SqPw0mB/BanVCMUgGgFJxERERERvyhzV72YmJgik0MYhkF6ejqhoaF8+OGH5Vq4c15+i9NhIwxwUyVMwUlERERExB/KHJzeeOONIsHJarVSvXp1OnToQExMTLkW7pyXH5z2ucOAI0SHaipyERERERF/KHNwGjx48BkohhTjygZ3NgD7XCHAEbU4iYiIiIj4SZnHOE2ZMoWZM2cWWz9z5kymTZtWLoUSfOObsNhIyTFbmjTGSURERETEP8ocnF588UWqVatWbH2NGjV44YUXyqVQgq+bHiHRpGa7AYhRVz0REREREb8oc3DasWMH9erVK7Y+MTGRnTt3lkuhhELBKYZDmXkA6qonIiIiIuInZQ5ONWrU4Lfffiu2fu3atVStWrVcCiUUCU6Hs8zgpK56IiIiIiL+UebgdNNNN/Hggw+yYMECPB4PHo+H+fPn89BDD3HTTTediTKem3JSzcfgaF9w0g1wRURERET8o8yz6o0ZM4YdO3Zw2WWXYbebb/d6vQwcOFBjnMpTfouTERLD4SwXgKYjFxERERHxkzIHp6CgIGbMmMGYMWNYs2YNISEhtGzZksTExDNRvnNXfnByB0WR5/YCGuMkIiIiIuIvZQ5OBRo2bEjDhg3LsyxSWH5wyrZHAhBksxIaZPNniUREREREzlllHuN03XXX8eKLLxZb/8orr3D99deXS6EEX3DKsEQAEBPmwGKx+LNEIiIiIiLnrDIHp0WLFtGnT59i66+88koWL15cLoUSfMEpzRIOQIwmhhARERER8ZsyB6eMjAyCgopfxDscDtLS0sqlUIIvOB02wgAFJxERERERfypzcGrRogUzZswotv6TTz6hWbNm5VIowRecDnnyg1OYZtQTEREREfGXMk8O8dRTT3Httdfy119/cemllwLwww8/8PHHH/PZZ5+VewHPWdmpAPztCQXy1OIkIiIiIuJHZQ5OV111FV9++SUvvPACn332GSEhIbRq1Yr58+cTGRl5Jsp47vG4Idfs9rgvLxgFJxERERER/zql6cj79OnjmyAiNTWVjz76iBEjRrB27Vo8Hk+5FvCclHPE9zQ5LxhII0b3cBIRERER8Zsyj3EqMH/+fG677TYSEhIYP348vXv3ZsWKFeVZtnNX/vgmnJEczDJvfhsTqjFOIiIiIiL+UqYWp927dzN16lQmT55MZmYmN9xwAy6Xi88//1wTQ5SnguAUEs3hrDwAtTiJiIiIiPhRqVucevfuTbNmzdi4cSNvvfUWe/fu5a233jqTZTt3+YJTDIcz84OTxjiJiIiIiPhNqVuc5s2bx4MPPsi9995Lw4YNz2SZpHBwynIBUEXBSURERETEb0rd4rRkyRLS09Np164dHTp0YPz48ezfv/9Mlu3clR+cPM5osl3mZBvRuo+TiIiIiIjflDo4dezYkXfffZfk5GTuuecePvnkE2rWrInX6yUpKYn09PQzWc5zS35wynGY07vbrRYinKc0AaKIiIiIiJSDMs+qFxoaypAhQ/jxxx9Zt24d//jHP3jxxRepUaMGV1111Zko47knJxWALGsEANGhQVgsFj8WSERERETk3HbK05EDNG7cmJdffpndu3czffr08iqT5Lc4ZeQHJ01FLiIiIiLiX6cVnArYbDYGDBjA7Nmzy+Nwkh+cjhjhgKYiFxERERHxt3IJTlLO8oNTqhEGqMVJRERERMTfFJwqovzgtN9jBqcqanESEREREfErBaeKqCA4uUMAc3IIERERERHxHwWnisbr9QWn5DwzOOnmtyIiIiIi/qXgVNHkpYPhBWBvbjAA0RrjJCIiIiLiVwpOFU12qvloD2FftnnvJo1xEhERERHxLwWniia/mx4h0RzOygM0xklERERExN8UnCoaX3CKITXLBajFSURERETE3xScKpr84OQNjiYj1w3oPk4iIiIiIv6m4FTR5AcnlyMKAKsFIoMVnERERERE/EnBqaLJD07ZjkjAHN9ktVr8WSIRERERkXOe34PThAkTqFevHsHBwbRt25YlS5aU6n1Lly7FbrfTunXrM1vAsy0/OGVZzeCkbnoiIiIiIv7n1+A0Y8YMRowYwRNPPMHq1avp0qULvXr1YufOnSd835EjRxg4cCCXXXbZWSrpWZQ/HXm6JRyAGM2oJyIiIiLid34NTq+//jp33nknQ4cOpWnTpowbN47atWszceLEE77vnnvu4ZZbbqFjx45nqaRnUU4qAKnkByfNqCciIiIi4nd2f504Ly+PlStXMmrUqCLre/bsyU8//XTc902ZMoW//vqLDz/8kDFjxpz0PLm5ueTm5vpep6WlAeByuXC5XKdY+vJTUIaCR1vWQazAQXcwAFHB9gpRTjm+Y+tQApPqsXJQPQY+1WHloHoMfOdKHZbl8/ktOB04cACPx0NsbGyR9bGxsaSkpJT4ni1btjBq1CiWLFmC3V66oo8dO5bRo0cXWz9v3jxCQ0PLXvAzJCkpCYDu+3YRCazfkw7AoZRdzJmzw48lk9IqqEMJbKrHykH1GPhUh5WD6jHwVfY6zMrKKvW+fgtOBSyWojPGGYZRbB2Ax+PhlltuYfTo0TRq1KjUx3/ssccYOXKk73VaWhq1a9emZ8+eREZGnnrBy4nL5SIpKYkePXrgcDiwb3kEAGvVenAA2rZoTO8u9fxcSjmRY+tQApPqsXJQPQY+1WHloHoMfOdKHRb0RisNvwWnatWqYbPZirUu7du3r1grFEB6ejorVqxg9erV3H///QB4vV4Mw8ButzNv3jwuvfTSYu9zOp04nc5i6x0OR4X6EfjKkz/GKcVttoZViwiuUOWU46tovyk5NarHykH1GPhUh5WD6jHwVfY6LMtn89vkEEFBQbRt27ZY819SUhKdOnUqtn9kZCTr1q1jzZo1vmXYsGE0btyYNWvW0KFDh7NV9DPHlQ3uHAB254QAmlVPRERERKQi8GtXvZEjR3L77bfTrl07OnbsyH//+1927tzJsGHDALOb3Z49e3j//fexWq20aNGiyPtr1KhBcHBwsfUBK/8eTlhsJGfbAM2qJyIiIiJSEfg1ON14440cPHiQ5557juTkZFq0aMGcOXNITEwEIDk5+aT3dKpUCoJTSAyHsswZPtTiJCIiIiLif36fHGL48OEMHz68xG1Tp0494XufffZZnn322fIvlL/kBycjJIb0Q24AYkIrb59SEREREZFA4dcb4MoxslMBcAeZs/1ZLBAVouAkIiIiIuJvCk4VSX6LU54jCoDIYAd2m6pIRERERMTfdFVekeQHp2y72eJURRNDiIiIiIhUCApOFUl+cMqwRgAQrfFNIiIiIiIVgoJTRZIfnNItZnCqohn1REREREQqBAWniiQ/OKUaYQBEKziJiIiIiFQICk4VSX5wOuQ1g1OVMHXVExERERGpCBScKpL84LTfHQqoxUlEREREpKJQcKpIclIB+NsVAmhWPRERERGRikLBqSLJvwHu3lwnADGaVU9EREREpEJQcKooPC7ITQNgT67Z4hSjrnoiIiIiIhWCglNFkXPE93RXltnSFKOueiIiIiIiFYKCU0WRY04MYTgjOZTjBdTiJCIiIiJSUSg4VRCW/PFN3uAYDMNcF60xTiIiIiIiFYKCU0WRPxW5KygKgAinHYdN1SMiIiIiUhHoyryiyJ+KPNcRCWh8k4iIiIhIRaLgVEFY8oNTti0/OKmbnoiIiIhIhaHgVFHkd9XLsIQDanESEREREalIFJwqivzJIdIKgpNm1BMRERERqTAUnCoIS/505IeNMEDBSURERESkIlFwqijyW5wOeQqCk8Y4iYiIiIhUFApOFUX+GKd97lBAY5xERERERCoSBacKoqCrXoorBFBXPRERERGRikTBqaLI76q3Nzc/OIWpq56IiIiISEWh4FQRGF7fDXB3ZzsBtTiJiIiIiFQkCk4VgN2bg8XwArAzxwxMVTTGSURERESkwlBwqgCC3JkAGPYQsr1mF71ozaonIiIiIlJhKDhVAA5PBgAeZzQAYUE2nHabH0skIiIiIiKFKThVAAUtTq6gKACiNb5JRERERKRCUXCqAApanHLskYDGN4mIiIiIVDQKThVAQYtTls0MThrfJCIiIiJSsSg4VQBBHjM4ZVjDAbU4iYiIiIhUNApOFYAjPzilGmZw0j2cREREREQqFgWnCsDhNsc4HTbCAHXVExERERGpaBScKoCCrnoH3aGAuuqJiIiIiFQ0Ck4VgCN/coh97hBA05GLiIiIiFQ0Ck4VQFD+dOTJLjM4VVFwEhERERGpUBScKoCC6cj35AYDGuMkIiIiIlLRKDj5m2H4ZtXbne0ENMZJRERERKSiUXDyN3c2NsMFwAGPOauepiMXEREREalYFJz8LTsVAMNqJ5Nggh1WQoJs/i2TiIiIiIgUoeDkbzmpALiDogCLWptERERERCogBSc/s2QfBiDPEQWom56IiIiISEWk4ORv+V31cmwRAMSEaUY9EREREZGKxu7vApzzcswWp8yC4KQWJxERETnLvF4veXl55XY8l8uF3W4nJycHj8dTbseVs6cy1WFQUBBW6+m3Fyk4+VlBV700i4KTiIiInH15eXls27YNr9dbbsc0DIO4uDh27dqFxWIpt+PK2VOZ6tBqtVKvXj2Cgk7vOlvByd/yJ4c4Qv5U5LqHk4iIiJwlhmGQnJyMzWajdu3a5fJXeTBbsDIyMggPDy+3Y8rZVVnq0Ov1snfvXpKTk6lTp85phUAFJ3/Lb3E65LuHk8Y4iYiIyNnhdrvJysoiISGB0NDQcjtuQde/4ODggL7oPpdVpjqsXr06e/fuxe1243Cc+rV2YH8LlYAlf3KIA14zOFVRi5OIiIicJQVjV063C5NIRVbw+z7dsVoKTv6W31Xv77wQAKI1xklERETOskAfwyJyIuX1+1Zw8rOCySGSXWZwqqLgJCIiIiJS4Sg4+Vt+i9OeHCcA0RrjJCIiInLWdevWjREjRpR6/+3bt2OxWFizZs0ZK5NULApO/pbf4nTAYw7I1Kx6IiIiIsdnsVhOuAwePPiUjvvFF1/w/PPPl3r/2rVrk5ycTIsWLU7pfKeiZ8+e2Gw2li1bdtbOKUf5PThNmDCBevXqERwcTNu2bVmyZMlx9/3iiy/o0aMH1atXJzIyko4dOzJ37tyzWNpy5nFhycsAINUIJ8hmJSzI5udCiYiIiFRcycnJvmXcuHFERkYWWfd///d/RfZ3uVylOm6VKlWIiIgodTlsNhtxcXHY7WdnkuqdO3fy888/c//99zNp0qSzcs4TKe33Wpn4NTjNmDGDESNG8MQTT7B69Wq6dOlCr1692LlzZ4n7L168mB49ejBnzhxWrlxJ9+7d6devH6tXrz7LJS8n+TPqAaQRRnSoQ4MzRURExG8MwyArz10uS3aep0z7G4ZRqjLGxcX5lqioKCwWi+91Tk4O0dHRfPrpp3Tr1o3g4GA+/PBDDh48yM0330ytWrUIDQ2lZcuWTJ8+vchxj+2qV7duXV544QWGDBlCREQEderU4b///a9v+7Fd9RYuXIjFYuGHH36gXbt2hIaG0qlTJzZv3lzkPGPGjKFGjRpEREQwdOhQRo0aRevWrU/6uadMmULfvn259957mTFjBpmZmUW2p6amcvfddxMbG0twcDAtWrTg66+/9m1funQpl1xyCaGhocTExHDFFVdw+PBh32cdN25ckeO1adOGF1980ffaYrHwzjvv0L9/f8LCwhgzZgwej4c777yTevXqERISQuPGjYsFV4DJkyfTvHlznE4n8fHx3H///QAMGTKEvn37FtnX7XYTFxfH5MmTT/qdnG1+vY/T66+/zp133snQoUMBGDduHHPnzmXixImMHTu22P7HVugLL7zAV199xf/+9z8uuOCCs1Hk8pXfTS/HGooXq6YiFxEREb/Kdnlo9rR/evNsfO4KQoPK59L00Ucf5bXXXmPKlCk4nU5ycnJo27Ytjz76KJGRkXzzzTfcfvvt1K9fnw4dOhz3OK+99hrPP/88jz/+OJ999hn33nsvXbt2pUmTJsd9zxNPPMFrr71G9erVGTZsGEOGDGHp0qUAfPTRR/z73/9mwoQJdO7cmU8++YTXXnuNevXqnfDzGIbBlClTePvtt2nSpAmNGjXi008/5Y477gDMey716tWL9PR0PvzwQ8477zw2btyIzWb2ZFqzZg2XXXYZQ4YM4c0338Rut7NgwYIyT8/9zDPPMHbsWN544w1sNhter5datWrx6aefUq1aNX766Sfuvvtu4uPjueGGGwCYOHEiI0eO5MUXX6RXr14cOXLE930MHTqUrl27kpycTHx8PABz5swhIyPD9/6KxG/BKS8vj5UrVzJq1Kgi63v27MlPP/1UqmN4vV7S09OpUqXKcffJzc0lNzfX9zotLQ0wmxf93cRoydiPHciyhAMQFWL3e5mk7ArqTHUX2FSPlYPqMfCpDs8ul8uFYRh4vV7f4i+ncv6C/Y99fOihhxgwYECRfUeOHOl7ft999/Htt9/y6aef0r59e9/6gu+iQK9evRg2bBgA//rXv3jjjTeYP38+jRo1KnLOwmV//vnn6dKlCwCPPPII/fr1Iysri+DgYN566y2GDBnCoEGDAHjyySeZN28eGRkZJ/zsSUlJZGVl0aNHD7xeL7feeiuTJk3yHWfevHn8+uuvbNiwgUaNGgFmK1JB+V566SXatWvH+PHjfcds2rRpke/s2M9e0ndy8803FxtD9swzz/ieJyYmsnTpUmbMmMF1110HmC1sI0eO5IEHHvDt17ZtW7xeLxdddBGNGzfm/fff51//+hdgtk5dd911hIaGltvv0ev1YhgGLpfLFyYLlOXfGr8FpwMHDuDxeIiNjS2yPjY2lpSUlFId47XXXiMzM/OEiXTs2LGMHj262Pp58+aV6x2yT0XskTVcBGRYzJvf5h45yJw5c/xaJjl1SUlJ/i6ClAPVY+Wgegx8qsOzw263ExcXR0ZGBnl5eRiGwc8jL/JLWVzZmaTllG3IQk5ODoZh+P4wnpFhjh1v0qSJbx2YNz594403mDVrFsnJyeTl5ZGbm4vT6fTt53a7ycvL8732er00atSoyHGqV6/O7t27SUtL850rMzOTtLQ0srKyAKhXr57vPZGRkQD89ddf1K5dm99//53BgwcXOWarVq1YvHhxkXXH+s9//sOAAQN85+jTpw+PPPIIK1eupGHDhvzyyy8kJCQQFxdX4nFWr15N//79j3sOr9dLTk5Ose8MID093beuWbNmxY4xefJkPvjgA3bt2kVOTg55eXm0bNmStLQ09u/fz969e7nooouOe+5bb72VyZMnc88997B//37mzJnDl19+ecLvo6zy8vLIzs5m8eLFuN3uItsKvtPS8GtXPSh+QyrDMEo1zmf69Ok8++yzfPXVV9SoUeO4+z322GNF/sKQlpZG7dq16dmzp+/H7C+WdRmwFdLzW5yanFeH3r2b+bVMUnYul4ukpCR69OiBw6Hp5AOV6rFyUD0GPtXh2ZWTk8OuXbsIDw8nODgYgKhyOK5hGKSnpxMREXFGx28HBwdjsVh813Th4eY1VY0aNYpc573yyiu88847vP7667Rs2ZKwsDAefvhhvF6vbz+73U5QUJDvtdVqJSIioshx7HY7DoeDyMhI37nCwsKIjIz0/UG+SpUqxcpTsI/FYiEkJKTIMR0OBzab7bjXpYcOHWLOnDm4XK4i4348Hg8zZ87kxRdfJCYmBqvVetxjhIWF4XQ6j7vdbrcX217Q2lO4DqtVq1Zkn08//ZQnnniCV199lYsuuoiIiAheffVVfv31V9/nBQgNDT3uue+66y5Gjx7Nhg0bWLZsGXXr1uXKK68scd9TlZOTQ0hICF27dvX9zguUJaD5LThVq1YNm81WrHVp3759xVqhjjVjxgzuvPNOZs6cyeWXX37CfZ1OJ06ns9h6h8Ph/3+Q88yKSsdscaoWHuz/MskpqxC/KTltqsfKQfUY+FSHZ4fH48FisWC1WrFay2/OsIKL7oJjnykFxy7psfB5f/zxR/r378/AgQN95fvzzz9p2rRpkf2OLW9J5T/2+yp4XtK5j13XuHFjVqxY4etiB7By5coi+x5r+vTp1KpViy+//LLI+h9++IGxY8fywgsv0KpVK3bv3s2ff/7p66pX2Pnnn8/8+fN57rnnSjxH9erVSUlJ8ZUhLS2Nbdu2FfsOjv1ely5dSqdOnbjvvvt867Zu3erbNyoqirp167JgwQIuu+yy4557wIABTJs2jZ9//pk77rij3H8zVqsVi8VS4r8rZfl3xm+z6gUFBdG2bdtiTfFJSUl06tTpuO+bPn06gwcP5uOPP6ZPnz5nuphnVrMBuG/9gs9s5ufQzW9FREREyl+DBg1ISkrip59+YtOmTdxzzz2lHhpSnh544AEmTZrEtGnT2LJlC2PGjOG33347YavcpEmTuO6662jRokWRZciQIaSmpvLNN99wySWX0LVrV6699lqSkpLYtm0b3377Ld999x1g9sBavnw5w4cP57fffuP3339n4sSJHDhwAIBLL72UDz74gCVLlrB+/XoGDRpUbCxQSRo0aMCKFSuYO3cuf/zxB0899RTLly8vss+zzz7La6+9xptvvsmWLVtYtWoVb731VpF9hg4dyrRp09i0aVORUFnR+HU68pEjR/Lee+8xefJkNm3axMMPP8zOnTt9g/Aee+wx318GwAxNAwcO5LXXXuOiiy4iJSWFlJQUjhw54q+PcHoi4zHqdmWdYc6koln1RERERMrfU089RZs2bbjiiivo1q0bcXFxxSaPOBtuvfVWHnvsMf75z3/Spk0btm3bxuDBg4t1HyuwcuVK1q5dy7XXXltsW0REBD179vTd0+nzzz+nffv23HzzzTRr1oxHHnnEN06pUaNGzJs3j7Vr13LhhRfSsWNHvvrqK989qB577DG6du1K37596d27NwMGDOC888476ecZNmwY11xzDTfeeCMdOnTg4MGDDB8+vMg+gwYNYty4cUyYMIHmzZvTt29ftmzZUmSfyy+/nPj4eK644goSEhJO/kX6icUo7aT5Z8iECRN4+eWXfXdefuONN+jatSsAgwcPZvv27SxcuBAw59dftGhRsWMMGjSIqVOnlup8aWlpREVFceTIEb+PcQKzL3f3F+eyO9PClMHt6d7k+OO1pGJyuVzMmTOH3r17q1tJAFM9Vg6qx8CnOjy7cnJy2LZtG/Xq1Tvuxfup8Hq9pKWlERkZeUa76lUGPXr0IC4ujg8++MDfRSnibNZhVlYWCQkJTJ48mWuuuabcj3+i33lZsoHfJ4cYPnx4sWRa4NgwVBCgKpvM/FkQY9TiJCIiIlJpZWVl8c4773DFFVdgs9mYPn0633///Tk7i6TX6yUlJYXXXnuNqKgorrrqKn8X6YT8HpwEMvNnRYzRGCcRERGRSstisTBnzhzGjBlDbm4ujRs35vPPPz/pZGeV1c6dO6lXrx61atVi6tSpvq6DFVXFLt05IMflIc9rDghUi5OIiIhI5RUSEsL333/v72JUGHXr1sXPo4bKRJ1O/exwltlPz261EOFUjhURERERqYgUnPwsNT84RYc6zugN4kRERERE5NQpOPnZ4aw8AKJDNL5JRERERKSiUnDys4IWJ41vEhERERGpuBSc/EwtTiIiIiIiFZ+Ck58VTA6hqchFRERERCouBSc/Oxqc1FVPRERE5Gzp1q0bI0aM8L2uW7cu48aNO+F7LBYLX3755Wmfu7yOI2eXgpOfFZ5VT0REREROrF+/fse9YezPP/+MxWJh1apVZT7u8uXLufvuu0+3eEU8++yztG7dutj65ORkevXqVa7nOp7s7GxiYmKoUqUK2dnZZ+WclZWCk5+lZptjnNRVT0REROTk7rzzTubPn8+OHTuKbZs8eTKtW7emTZs2ZT5u9erVCQ0NLY8inlRcXBxOp/OsnOvzzz+nRYsWNGvWjC+++OKsnPN4DMPA7Xb7tQynQ8HJzw6rxUlEREQqCsOAvMzyWVxZZdvfMEpVxL59+1KjRg2mTp1aZH1WVhYzZszgzjvv5ODBg9x8883UqlWL0NBQWrZsyfTp00943GO76m3ZsoWuXbsSHBxMs2bNSEpKKvaeRx99lEaNGhEaGkr9+vV56qmncLnMa7upU6cyevRo1q5di8ViwWKx+Mp8bFe9devWcemllxISEkLVqlW5++67ycjI8G0fPHgwAwYM4NVXXyU+Pp6qVaty3333+c51IpMmTeK2227jtttuY9KkScW2b9iwgT59+hAZGUlERARdunThr7/+8m2fPHkyzZs3x+l0Eh8fz/333w/A9u3bsVgsrFmzxrdvamoqFouFhQsXArBw4UIsFgtz586lXbt2OJ1OlixZwl9//UX//v2JjY0lPDyc9u3b8/333xcpV25uLo888gi1a9fG6XTSsGFDJk2ahGEYNGjQgFdffbXI/uvXr8dqtRYpe3mzn7EjS6kUBKcqGuMkIiIi/ubKghcSTvswViC6rG96fC8EhZ10N7vdzsCBA5k6dSpPP/00FosFgJkzZ5KXl8ett95KVlYWbdu25dFHHyUyMpJvvvmG22+/nfr169OhQ4eTnsPr9XLNNddQrVo1li1bRlpaWpHxUAUiIiKYOnUqCQkJrFu3jrvuuouIiAgeeeQRbrzxRtavX893333nCwVRUVHFjpGVlcWVV17JRRddxPLly9m3bx9Dhw7l/vvvLxIOFyxYQHx8PAsWLODPP//kxhtvpHXr1tx1113H/Rx//fUXP//8M1988QWGYTBixAi2bt1K/fr1AdizZw9du3alW7duzJ8/n8jISJYuXeprFZo0aRJPPvkkL774Ir169eLIkSMsXbr0pN/fsR555BFeffVV6tevT3R0NLt376Z3796MGTOG4OBgpk2bRr9+/di8eTN16tQBYODAgfz888+8+eabtGrVim3btnHgwAEsFgtDhgxhypQp/POf//SdY/LkyXTp0oXzzjuvzOUrLQUnP/NNR64WJxEREZFSGTJkCK+88goLFy6ke/fugHnhfM011xATE0NMTEyRi+oHHniA7777jpkzZ5YqOH3//fds2rSJ7du3U6tWLQBeeOGFYuOSnnzySd/zunXr8o9//IMZM2bwyCOPEBISQnh4OHa7nbi4uOOe66OPPiI7O5v333+fsDAzOI4fP55+/frx0ksvERsbC0BMTAzjx4/HZrPRpEkT+vTpww8//HDC4DR58mR69epFTEwMAFdeeSWTJ09mzJgxALz99ttERUXxySef4HCY16KNGjUCzPD42muvMXLkSB566CHfMdu3b3/S7+9Yzz33HD169PC9rlq1Kq1atfK9HjNmDLNmzWL27Nncf//9/PHHH3z66ackJSX5xrMVhD2AO+64g6effppff/2VCy+8EJfLxYcffsgrr7xS5rKVhYKTH+W5vWTmegDNqiciIiIVgCPUbPk5TV6vl7T0dCIjIrBaSzkyxFH68UVNmjShU6dOTJ48me7du/PXX3+xZMkS5s2bB4DH4+HFF19kxowZ7Nmzh9zcXHJzc33B5GQ2bdpEnTp1fKEJoGPHjsX2++yzzxg3bhx//vknGRkZuN1uIiMjS/05Cs7VqlWrImXr3LkzXq+XzZs3+4JT8+bNsdlsvn3i4+NZt27dcY/r8XiYNm0a//d//+dbd9ttt/Hwww8zevRobDYba9asoUuXLr7QVNi+fftITk7m0ksvLdPnKUm7du2KvM7MzGT06NF8/fXX7N27F7fbTXZ2Njt37gRgzZo12Gw2LrnkkhKPFx8fT58+fZg8eTIXXnghX3/9NTk5OVx//fWnXdYT0RgnP0rNb22yYBAZrAwrIiIifmaxmN3lymNxhJZt//wud6V155138vnnn5OWlsaUKVNITEzksssuA+C1117jjTfe4JFHHmH+/PmsWbOGK664gry8vFId2yhhvJXlmPItW7aMm266iV69evH111+zevVqnnjiiVKfo/C5jj12Sec8NtxYLBa8Xu9xjzt37lz27NnDjTfeiN1ux263c9NNN7F7925fwAwJCTnu+0+0DfAF4sLf1fHGXB0bWP/1r3/x+eef8+9//5slS5awZs0aWrZs6fvuTnZugKFDh/LJJ5+QnZ3NlClTuPHGG8/45B4KTn5UML4p1A5Wa9n+sRARERE5l91www3YbDY+/vhjpk2bxh133OELGkuWLKF///7cdttttGrVivr167Nly5ZSH7tZs2bs3LmTvXuPtr79/PPPRfZZunQpiYmJPPHEE7Rr146GDRsWm+kvKCgIj8dz0nOtWbOGzMzMIse2Wq2+bnOnYtKkSdx0002sWbOmyHLrrbf6Jok4//zzWbJkSYmBJyIigjp16jB//vwSj1+9enXAnFq9QOGJIk5kyZIlDB48mKuvvpqWLVsSFxfH9u3bfdtbtmyJ1+tl0aJFxz1G7969CQsLY+LEiXz77bcMGTKkVOc+HQpOfnQo00zVYWpsEhERESmT8PBwbrzxRh5//HH27t3L4MGDfdsaNGhAUlISP/30E5s2beKee+4hJSWl1Me+/PLLady4MQMHDmTt2rUsWbKEJ554osg+DRo0YOfOnXzyySf89ddfvPnmm8yaNavIPnXr1mXbtm2sWbOGAwcOkJubW+xct956K8HBwQwaNIj169ezYMECHnjgAW6//XZfN72y2r9/P//73/8YNGgQLVq0KLIMGjSI2bNns3//fu6//37S0tK46aabWLFiBVu2bOGDDz5g8+bNAIwaNYrXX3+dN998ky1btrBq1SreeustwGwVuuiii3jxxRfZuHEjixcvLjLm60QaNGjAF198wZo1a1i7di233HJLkdazunXrMmjQIIYMGcKXX37Jtm3bWLhwIZ9++qlvH5vNxuDBg3nsscdo0KBBiV0py5uCkx8VdNUL07wQIiIiImV25513cvjwYS6//HLfbGwATz31FG3atOGKK66gW7duxMXFMWDAgFIf12q1MmvWLHJzc7nwwgsZOnQo//73v4vs079/fx5++GHuv/9+WrduzU8//cRTTz1VZJ9rr72WK6+8ku7du1O9evUSp0QPDQ1l7ty5HDp0iPbt23Pddddx2WWXMX78+LJ9GYUUTDRR0HWxsO7duxMREcEHH3xA1apVmT9/PhkZGVxyySW0bduWd99919ct8Oabb+b1119nwoQJNG/enL59+xZpuZs8eTIul4t27drx0EMP+SadOJk33niDmJgYOnXqRL9+/bjiiiuK3Xtr4sSJXHfddQwfPpwmTZpw1113FWmVA7P+8/LyzkprE4DFKKkTZyWWlpZGVFQUR44cKfPgvfL20S87eGLWelrEeJk18soSB+ZJxedyuZgzZw69e/dWHQYw1WPloHoMfKrDsysnJ4dt27ZRr149goODy+24Xq+XtLQ0IiMjSz85hFQogVCHS5cupVu3buzevfuErXMn+p2XJRuok5gfxYQG0S4xmqruQ/4uioiIiIhIQMjNzWXXrl089dRT3HDDDafcpbGsKmZ8PEf0bhnP9KEX0rvO8WdEERERERGRo6ZPn07jxo05cuQIL7/88lk7r4KTiIiIiIgEjMGDB+PxeFi5ciU1a9Y8a+dVcBIRERERETkJBScRERGRc9w5NleYnGPK6/et4CQiIiJyjrLZbADk5eX5uSQiZ07B77vg936qNKueiIiIyDnKbrcTGhrK/v37cTgc5TbttNfrJS8vj5ycnAo7lbWcWGWpQ6/Xy/79+wkNDcVuP73oo+AkIiIico6yWCzEx8ezbds2duzYUW7HNQyD7OxsQkJCsFgs5XZcOXsqUx1arVbq1Klz2p9DwUlERETkHBYUFETDhg3Ltbuey+Vi8eLFdO3aVTcyDlCVqQ6DgoLKpdVMwUlERETkHGe1WgkODi6349lsNtxuN8HBwQF/0X2uUh0WF7gdFkVERERERM4SBScREREREZGTUHASERERERE5iXNujFPBDbDS0tL8XBKTy+UiKyuLtLQ09R8NUKrDykH1WDmoHgOf6rByUD0GvnOlDgsyQWluknvOBaf09HQAateu7eeSiIiIiIhIRZCenk5UVNQJ97EYpYlXlYjX62Xv3r1ERERUiDnp09LSqF27Nrt27SIyMtLfxZFToDqsHFSPlYPqMfCpDisH1WPgO1fq0DAM0tPTSUhIOOmU5edci5PVaqVWrVr+LkYxkZGRlfpHeS5QHVYOqsfKQfUY+FSHlYPqMfCdC3V4spamApocQkRERERE5CQUnERERERERE5CwcnPnE4nzzzzDE6n099FkVOkOqwcVI+Vg+ox8KkOKwfVY+BTHRZ3zk0OISIiIiIiUlZqcRIRERERETkJBScREREREZGTUHASERERERE5CQUnERERERGRk1Bw8qMJEyZQr149goODadu2LUuWLPF3keQEFi9eTL9+/UhISMBisfDll18W2W4YBs8++ywJCQmEhITQrVs3NmzY4J/CSonGjh1L+/btiYiIoEaNGgwYMIDNmzcX2Uf1WPFNnDiR888/33dTxo4dO/Ltt9/6tqsOA8/YsWOxWCyMGDHCt071WPE9++yzWCyWIktcXJxvu+owcOzZs4fbbruNqlWrEhoaSuvWrVm5cqVvu+rSpODkJzNmzGDEiBE88cQTrF69mi5dutCrVy927tzp76LJcWRmZtKqVSvGjx9f4vaXX36Z119/nfHjx7N8+XLi4uLo0aMH6enpZ7mkcjyLFi3ivvvuY9myZSQlJeF2u+nZsyeZmZm+fVSPFV+tWrV48cUXWbFiBStWrODSSy+lf//+vv+Jqw4Dy/Lly/nvf//L+eefX2S96jEwNG/enOTkZN+ybt063zbVYWA4fPgwnTt3xuFw8O2337Jx40Zee+01oqOjffuoLvMZ4hcXXnihMWzYsCLrmjRpYowaNcpPJZKyAIxZs2b5Xnu9XiMuLs548cUXfetycnKMqKgo45133vFDCaU09u3bZwDGokWLDMNQPQaymJgY47333lMdBpj09HSjYcOGRlJSknHJJZcYDz30kGEY+m8xUDzzzDNGq1atStymOgwcjz76qHHxxRcfd7vq8ii1OPlBXl4eK1eupGfPnkXW9+zZk59++slPpZLTsW3bNlJSUorUqdPp5JJLLlGdVmBHjhwBoEqVKoDqMRB5PB4++eQTMjMz6dixo+owwNx333306dOHyy+/vMh61WPg2LJlCwkJCdSrV4+bbrqJrVu3AqrDQDJ79mzatWvH9ddfT40aNbjgggt49913fdtVl0cpOPnBgQMH8Hg8xMbGFlkfGxtLSkqKn0olp6Og3lSngcMwDEaOHMnFF19MixYtANVjIFm3bh3h4eE4nU6GDRvGrFmzaNasmeowgHzyySesWrWKsWPHFtumegwMHTp04P3332fu3Lm8++67pKSk0KlTJw4ePKg6DCBbt25l4sSJNGzYkLlz5zJs2DAefPBB3n//fUD/PRZm93cBzmUWi6XIa8Mwiq2TwKI6DRz3338/v/32Gz/++GOxbarHiq9x48asWbOG1NRUPv/8cwYNGsSiRYt821WHFduuXbt46KGHmDdvHsHBwcfdT/VYsfXq1cv3vGXLlnTs2JHzzjuPadOmcdFFFwGqw0Dg9Xpp164dL7zwAgAXXHABGzZsYOLEiQwcONC3n+pSLU5+Ua1aNWw2W7GUvm/fvmJpXgJDwSxCqtPA8MADDzB79mwWLFhArVq1fOtVj4EjKCiIBg0a0K5dO8aOHUurVq34v//7P9VhgFi5ciX79u2jbdu22O127HY7ixYt4s0338Rut/vqSvUYWMLCwmjZsiVbtmzRf4sBJD4+nmbNmhVZ17RpU9+EZarLoxSc/CAoKIi2bduSlJRUZH1SUhKdOnXyU6nkdNSrV4+4uLgidZqXl8eiRYtUpxWIYRjcf//9fPHFF8yfP5969eoV2a56DFyGYZCbm6s6DBCXXXYZ69atY82aNb6lXbt23HrrraxZs4b69eurHgNQbm4umzZtIj4+Xv8tBpDOnTsXuzXHH3/8QWJiIqD/Nxbhr1kpznWffPKJ4XA4jEmTJhkbN240RowYYYSFhRnbt2/3d9HkONLT043Vq1cbq1evNgDj9ddfN1avXm3s2LHDMAzDePHFF42oqCjjiy++MNatW2fcfPPNRnx8vJGWlubnkkuBe++914iKijIWLlxoJCcn+5asrCzfPqrHiu+xxx4zFi9ebGzbts347bffjMcff9ywWq3GvHnzDMNQHQaqwrPqGYbqMRD84x//MBYuXGhs3brVWLZsmdG3b18jIiLCdy2jOgwMv/76q2G3241///vfxpYtW4yPPvrICA0NNT788EPfPqpLk4KTH7399ttGYmKiERQUZLRp08Y3JbJUTAsWLDCAYsugQYMMwzCn63zmmWeMuLg4w+l0Gl27djXWrVvn30JLESXVH2BMmTLFt4/qseIbMmSI79/O6tWrG5dddpkvNBmG6jBQHRucVI8V34033mjEx8cbDofDSEhIMK655hpjw4YNvu2qw8Dxv//9z2jRooXhdDqNJk2aGP/973+LbFddmiyGYRj+aesSEREREREJDBrjJCIiIiIichIKTiIiIiIiIieh4CQiIiIiInISCk4iIiIiIiInoeAkIiIiIiJyEgpOIiIiIiIiJ6HgJCIiIiIichIKTiIiIiIiIieh4CQiInICFouFL7/80t/FEBERP1NwEhGRCmvw4MFYLJZiy5VXXunvoomIyDnG7u8CiIiInMiVV17JlClTiqxzOp1+Ko2IiJyr1OIkIiIVmtPpJC4ursgSExMDmN3oJk6cSK9evQgJCaFevXrMnDmzyPvXrVvHpZdeSkhICFWrVuXuu+8mIyOjyD6TJ0+mefPmOJ1O4uPjuf/++4tsP3DgAFdffTWhoaE0bNiQ2bNn+7YdPnyYW2+9lerVqxMSEkLDhg2LBT0REQl8Ck4iIhLQnnrqKa699lrWrl3Lbbfdxs0338ymTZsAyMrK4sorryQmJobly5czc+ZMvv/++yLBaOLEidx3333cfffdrFu3jtmzZ9OgQYMi5xg9ejQ33HADv/32G7179+bWW2/l0KFDvvNv3LiRb7/9lk2bNjFx4kSqVat29r4AERE5KyyGYRj+LoSIiEhJBg8ezIcffkhwcHCR9Y8++ihPPfUUFouFYcOGMXHiRN+2iy66iDZt2jBhwgTeffddHn30UXbt2kVYWBgAc+bMoV+/fuzdu5fY2Fhq1qzJHXfcwZgxY0osg8Vi4cknn+T5558HIDMzk4iICObMmcOVV17JVVddRbVq1Zg8efIZ+hZERKQi0BgnERGp0Lp3714kGAFUqVLF97xjx45FtnXs2JE1a9YAsGnTJlq1auULTQCdO3fG6/WyefNmLBYLe/fu5bLLLjthGc4//3zf87CwMCIiIti3bx8A9957L9deey2rVq2iZ8+eDBgwgE6dOp3SZxURkYpLwUlERCq0sLCwYl3nTsZisQBgGIbveUn7hISElOp4Doej2Hu9Xi8AvXr1YseOHXzzzTd8//33XHbZZdx33328+uqrZSqziIhUbBrjJCIiAW3ZsmXFXjdp0gSAZs2asWbNGjIzM33bly5ditVqpVGjRkRERFC3bl1++OGH0ypD9erVfd0Kx40bx3//+9/TOp6IiFQ8anESEZEKLTc3l5SUlCLr7Ha7bwKGmTNn0q5dOy6++GI++ugjfv31VyZNmgTArbfeyjPPPMOgQYN49tln2b9/Pw888AC33347sbGxADz77LMMGzaMGjVq0KtXL9LT01m6dCkPPPBAqcr39NNP07ZtW5o3b05ubi5ff/01TZs2LcdvQEREKgIFJxERqdC+++474uPji6xr3Lgxv//+O2DOePfJJ58wfPhw4uLi+Oijj2jWrBkAoaGhzJ07l4ceeoj27dsTGhrKtddey+uvv+471qBBg8jJyeGNN97gn//8J9WqVeO6664rdfmCgoJ47LHH2L59OyEhIXTp0oVPPvmkHD65iIhUJJpVT0REApbFYmHWrFkMGDDA30UREZFKTmOcRERERERETkLBSURERERE5CQ0xklERAKWepuLiMjZohYnERERERGRk1BwEhEREREROQkFJxERERERkZNQcBIRERERETkJBScREREREZGTUHASERERERE5CQUnERERERGRk1BwEhEREREROYn/B+jpduOPV3L1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_history(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "060cdfd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3421/3421 [==============================] - 41s 12ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Predict\n",
    "y_pred_probs = model.predict(X)  # shape: (num_samples, num_classes)\n",
    "y_pred = np.argmax(y_pred_probs, axis=1)\n",
    "\n",
    "# Step 2: Confusion matrix\n",
    "cm = confusion_matrix(y, y_pred)\n",
    "\n",
    "# Step 3: Plot\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=['N','S','V','F','U'])  # MIT-BIH 5 classes\n",
    "disp.plot(cmap=\"Blues\", values_format=\"d\")\n",
    "plt.title(\"Confusion Matrix on MIT-BIH Test Set\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf-gpu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
